Publications

2024

• Jing Hu, Lingfei Wu, Yu Chen, Po Hu, and Mohammed J. Zaki. GraphFlow+: exploiting conversation flow in conversational machine comprehension with graph neural networks. Machine Intelligence Research, 21(2):272–282, April 2024. doi:http://doi.org/10.1007/s11633-023-1421-0.
  [abstract▼] [full text] [BibTeX▼]

  The conversation machine comprehension (MC) task aims to answer questions in the multi-turn conversation for a single passage. However, recent approaches don't exploit information from historical conversations effectively, which results in some references and ellipsis in the current question cannot be recognized. In addition, these methods do not consider the rich semantic relationships between words when reasoning about the passage text. In this paper, we propose a novel model GraphFlow+, which constructs a context graph for each conversation turn and uses a unique recurrent graph neural network (GNN) to model the temporal dependencies between the context graphs of each turn. Specifically, we exploit three different ways to construct text graphs, including the dynamic graph, static graph, and hybrid graph that combines the two. Our experiments on CoQA, QuAC and DoQA show that the GraphFlow+ model can outperform the state-of-the-art approaches.

  @article{2024-mir,
      author = "Hu, Jing and Wu, Lingfei and Chen, Yu and Hu, Po and Zaki, Mohammed J.",
      title = "{GraphFlow+}: Exploiting Conversation Flow in Conversational Machine Comprehension with Graph Neural Networks",
      journal = "Machine Intelligence Research",
      year = "2024",
      volume = "21",
      number = "2",
      pages = "272--282",
      month = "April",
      doi = "http://doi.org/10.1007/s11633-023-1421-0",
      KeyWord = "Journal,GraphMining,NeuralNetworks,QA"
  }
  

• Jonathan J. Harris and Mohammed J. Zaki. Neural models for generating natural language summaries from temporal personal health data. Journal of Healthcare Informatics Research, Jan 2024. doi:https://doi.org/10.1007/s41666-023-00158-x.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  With an increased interest in the production of personal health technologies designed to track user data (e.g., nutrient intake, step counts), there is now more opportunity, than ever to surface meaningful behavioral insights to everyday users in the form of, natural language. This knowledge can increase their behavioral awareness and allow, them to take action to meet their health goals. It can also bridge the gap between the, vast collection of personal health data and the summary generation required to describe, an individual's behavioral tendencies. Previous work has focused on rule-based time-series data summarization methods designed to generate natural language summaries of interesting patterns found within temporal personal health data. We examine recur-rent, convolutional, and Transformer-based encoder-decoder models to automatically generate natural language summaries from numeric temporal personal health data. We showcase the effectiveness of our models on real user health data logged in MyFitnessPal (Weber and Achananuparp 2016) and show that we can automatically generate high-quality natural language summaries. Our work serves as a first step towards the ambitious goal of automatically generating novel and meaningful temporal summaries from personal health data.

  @article{2024-jhir,
      author = "Harris, Jonathan J. and Zaki, Mohammed J.",
      title = "Neural Models for Generating Natural Language Summaries from Temporal Personal Health Data",
      journal = "Journal of Healthcare Informatics Research",
      year = "2024",
      month = "Jan",
      doi = "https://doi.org/10.1007/s41666-023-00158-x",
      customlinkgithubcode = "https://github.com/neato47/Neural-Numeric-To-Text-Generation",
      KeyWord = "Journal,TemporalSummaries,DeepLearning,PersonalHealth"
  }
  

• Bolun "Namir" Xia, Vipula D. Rawte, Mohammed J. Zaki, and Aparna Gupta. Fetilda: an effective framework for fin-tuned embeddings for long financial text documents. ACM Transactions on Knowledge Discovery from Data, Apr 2024. URL: http://arxiv.org/abs/2206.06952, doi:10.1145/3657299.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  In the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosure documents that companies submit on a regular basis to regulatory agencies, such as the Securities and Exchange Commission (SEC). These documents are typically very long and tend to contain valuable soft information about a company’s performance that is not present in quantitative predictors. It is therefore of great interest to learn predictive models from these long textual documents, especially for forecasting numerical key performance indicators (KPIs). In recent years, there has been a great progress in natural language processing via pre-trained language models (LMs) learned from large corpora of textual data. This prompts the important question of whether they can be used effectively to produce representations for long documents, as well as how we can evaluate the effectiveness of representations produced by various LMs. Our work focuses on answering this critical question, namely the evaluation of the efficacy of various LMs in extracting useful soft information from long textual documents for prediction tasks. In this paper, we propose and implement a deep learning evaluation framework that utilizes a sequential chunking approach combined with an attention mechanism. We perform an extensive set of experiments on a collection of 10-K reports submitted annually by US banks, and another dataset of reports submitted by US companies, in order to investigate thoroughly the performance of different types of language models. Overall, our framework using LMs outperforms strong baseline methods for textual modeling as well as for numerical regression. Our work provides better insights into how utilizing pre-trained domain-specific and fine-tuned long-input LMs for representing long documents can improve the quality of representation of textual data, and therefore, help in improving predictive analyses

  @article{2024-tkdd,
      Author = {Xia, Bolun "Namir" and Rawte, Vipula D. and Zaki, Mohammed J. and Gupta, Aparna},
      Title = "FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents",
      Year = "2024",
      month = "Apr",
      journal = "ACM Transactions on Knowledge Discovery from Data",
      url = "http://arxiv.org/abs/2206.06952",
      doi = "10.1145/3657299",
      customlinkgithubcode = "https://github.com/Namir0806/FETILDA",
      KeyWord = "WordEmbeddings,FinancialText"
  }
  

• Md. Shamim Hussain, Mohammed J. Zaki, and Dharmashankar Subramanian. Triplet interaction improves graph transformers: accurate molecular graph learning with triplet graph transformers. In 41st International Conference on Machine Learning. Jul 2024.
  [abstract▼] [full text] [Arxiv] [githubcode] [BibTeX▼]

  Graph transformers typically lack third-order interactions, limiting their geometric understanding which is crucial for tasks like molecular geometry prediction. We propose the Triplet Graph Transformer (TGT) that enables direct communication between pairs within a 3-tuple of nodes via novel triplet attention and aggregation mechanisms. TGT is applied to molecular property prediction by first predicting interatomic distances from 2D graphs and then using these distances for downstream tasks. A novel three-stage training procedure and stochastic inference further improve training efficiency and model performance. Our model achieves new state-of-the-art (SOTA) results on open challenge benchmarks PCQM4Mv2 and OC20 IS2RE. We also obtain SOTA results on QM9, MOLPCBA, and LITPCBA molecular property prediction benchmarks via transfer learning. We also demonstrate the generality of TGT with SOTA results on the traveling salesman problem (TSP).

  @inproceedings{2024-icml,
      author = "Hussain, Md. Shamim and Zaki, Mohammed J. and Subramanian, Dharmashankar",
      title = "Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers",
      booktitle = "41st International Conference on Machine Learning",
      year = "2024",
      month = "Jul",
      customlinkArxiv = "https://arxiv.org/abs/2402.04538",
      customlinkgithubcode = "https://github.com/shamim-hussain/tgt",
      KeyWord = "Conference,GraphNN,NeuralNetworks"
  }
  

• Qitong Wang, Georgios Kollias, Vasileios Kalantzis, Naoki Abe, and Mohammed J. Zaki. Directed graph transformers. Transactions on Machine Learning Research, Jul 2024. URL: https://openreview.net/pdf?id=otTFPjziiK.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  In this paper, we address the problem of capturing graph directionality using transformers. Most existing graph transformers typically capture distances between graph nodes and do not take edge direction into account. This is a limiting assumption since many graph applications need to exploit sophisticated relationships in graph data, such as time, causality, or generic dependency constraints. We introduce a novel graph transformer architecture that explicitly takes into account the directionality between connected graph nodes. To achieve this, we make use of dual encodings to represent both potential roles, i.e., source or target, of each pair of vertices linked by a directed edge. These encodings are learned by leveraging the latent adjacency information extracted from a directional attention module, localized with k-hop neighborhood information. Extensive experiments on synthetic and real graph datasets show that our approach can have significant accuracy gains over previous graph transformer (GT) and graph neural network (GNN) approaches, providing state-of-the-art (SOTA) results on inherently directed graphs.

  @article{2024-tmlr,
      author = "Wang, Qitong and Kollias, Georgios and Kalantzis, Vasileios and Abe, Naoki and Zaki, Mohammed J.",
      title = "Directed Graph Transformers",
      journal = "Transactions on Machine Learning Research",
      issn = "2835-8856",
      year = "2024",
      month = "Jul",
      KeyWord = "Journal,GraphNN,NeuralNetworks",
      customlinkgithubcode = "https://github.com/Qitong-Wang/Directed-Graph-Transformers",
      url = "https://openreview.net/pdf?id=otTFPjziiK"
  }
  

• Namir Xia, Mohammed J. Zaki, and Aparna Gupta. Semantic graph learning for trend prediction from long financial documents. In IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr). Oct 2024.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  The advent of large language models (LLMs) has initiated much research into their various financial applications. However, in applying LLMs on long documents, semantic relations are not explicitly incorporated, and a full or arbitrarily sparse attention operation is employed. In recent years, progress has been made in Abstract Meaning Representation (AMR), which is a graph-based representation of text to preserve its semantic relations. Since AMR can represent semantic relationships at a deeper level, it can be beneficially utilized by graph neural networks (GNNs) for constructing effective document-level graph representations built upon LLM embeddings to predict target metrics in the financial domain. We propose FLAG: Financial Long document classification via AMR-based GNN, an AMR graph based framework to generate document-level embeddings for long financial document classification. We construct document-level graphs from sentence-level AMR graphs, endow them with specialized LLM word embeddings in the financial domain, apply a deep learning mechanism that utilizes a GNN, and examine the efficacy of our AMR-based approach in predicting labeled target data from long financial documents. Extensive experiments are conducted on a dataset of quarterly earnings calls transcripts of companies in various sectors of the economy, as well as on a corpus of more recent earnings calls of companies in the S&P 1500 Composite Index. We find that our AMR-based approach outperforms fine-tuning LLMs directly on text in predicting stock price movement trends at different time horizons in both datasets. Our work also outperforms previous work utilizing document graphs and GNNs for text classification

  @inproceedings{2024-cifer,
      author = "Xia, Namir and Zaki, Mohammed J. and Gupta, Aparna",
      title = "Semantic Graph Learning for Trend Prediction from Long Financial Documents",
      booktitle = "IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)",
      customlinkgithubcode = "https://github.com/Namir0806/FLAG",
      month = "Oct",
      year = "2024",
      KeyWord = "Conference,TextMining"
  }
  

• Fnu Mohbat and Mohammed J. Zaki. Llava-chef: a multi-modal generative model for food recipes. In ACM Conference on Information and Knowledge Management (CIKM). Oct 2024. doi:https://doi.org/10.1145/3627673.3679562.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  n the rapidly evolving landscape of online recipe sharing within a globalized context, there has been a notable surge in research towards comprehending and generating food recipes. Recent advancements in large language models (LLMs) like GPT-2 [40] and LLaVA [ 32 ] have paved the way for Natural Language Processing (NLP) approaches to delve deeper into various facets of food-related tasks, encompassing ingredient recognition and comprehensive recipe generation. Despite impressive performance and multi-modal adaptability of LLMs, domain-specific training remains paramount for their effective application. This work evaluates existing LLMs for recipe generation and proposes LLaVA-Chef, a novel model trained on a curated dataset of diverse recipe prompts in a multistage approach. First, we refine the mapping of visual food image embeddings to the language space. Second, we adapt LLaVA to the food domain by fine-tuning it on relevant recipe data. Third, we utilize diverse prompts to enhance the model’s recipe comprehension. Finally, we improve the linguistic quality of generated recipes by penalizing the model with a custom loss function. LLaVA-Chef demonstrates impressive improvements over pretrained LLMs and prior works. A detailed qualitative analysis reveals that LLaVA-Chef generates more detailed recipes with precise ingredient mentions, compared to existing approaches.

  @inproceedings{2024-cikm,
      author = "Mohbat, Fnu and Zaki, Mohammed J.",
      title = "LLaVA-Chef: A Multi-modal Generative Model for Food Recipes",
      booktitle = "ACM Conference on Information and Knowledge Management (CIKM)",
      doi = "https://doi.org/10.1145/3627673.3679562",
      customlinkgithubcode = "https://github.com/mohbattharani/LLaVA-Chef",
      month = "Oct",
      year = "2024"
  }
  

2023

• Diya Li, Mohammed J. Zaki, and Ching-Hua Chen. Health-guided recipe recommendation over knowledge graphs. Journal of Web Semantics, Special Issue on Knowledge Graphs and Information Retrieval, 75:100743, Jan 2023. doi:https://doi.org/10.1016/j.websem.2022.100743.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  While the availability of large-scale online recipe collections presents opportunities for health consumers to access a wide variety of recipes, it can be challenging for them to discover relevant recipes. Whereas most recommender systems are designed to offer selections consistent with users' past behavior, it remains an open problem to offer selections that can help users' transition from one type of behavior to another, intentionally. In this paper, we introduce health-guided recipe recommendation as a way to incrementally shift users towards healthier recipe options while respecting the preferences reflected in their past choices. Introducing a knowledge graph (KG) into recommender systems as side information has attracted great interest, but its use in recipe recommendation has not been studied. To fill this gap, we consider the task of recipe recommendation over knowledge graphs. In particular, we jointly learn recipe representations via graph neural networks over two graphs extracted from a large-scale Food KG, which capture different semantic relationships, namely, user preferences and recipe healthiness, respectively. To integrate the nutritional aspects into recipe representations and the recommendation task, instead of simple fusion, we utilize a knowledge transfer scheme to enable the transfer of useful semantic information across the preferences and healthiness aspects. Experimental results on two large real-world recipe datasets showcase our model's ability to recommend tasty as well as healthy recipes to users.

  @article{2023-jows,
      author = "Li, Diya and Zaki, Mohammed J. and Chen, Ching-Hua",
      title = "Health-guided recipe recommendation over knowledge graphs",
      journal = "Journal of Web Semantics, Special Issue on Knowledge Graphs and Information Retrieval",
      volume = "75",
      pages = "100743",
      year = "2023",
      month = "Jan",
      DOI = "https://doi.org/10.1016/j.websem.2022.100743",
      customlinkgithubcode = "https://github.com/DiyaLI916/recipe\_recommendation",
      KeyWord = "Journal,NeuralNetworks,RecipeRecommendation,KnowledgeGraphs,FoodComputing,Health"
  }
  

• Md. Shamim Hussain, Mohammed J. Zaki, and Dharmashankar Subramanian. The information pathways hypothesis: transformers are dynamic self-ensembles. In 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Aug 2023. URL: https://dl.acm.org/doi/10.1145/3580305.3599520.
  [abstract▼] [full text] [Arxiv] [githubcode] [BibTeX▼]

  Transformers use the dense self-attention mechanism which gives a lot of flexibility for long-range connectivity. Over multiple layers of a deep transformer, the number of possible connectivity patterns increases exponentially. However, very few of these contribute to the performance of the network, and even fewer are essential. We hypothesize that there are sparsely connected sub-networks within a transformer, called information pathways which can be trained independently. However, the dynamic (i.e., input-dependent) nature of these pathways makes it difficult to prune dense self-attention during training. But the overall distribution of these pathways is often predictable. We take advantage of this fact to propose Stochastically Subsampled self-Attention (SSA) – a general-purpose training strategy for transformers that can reduce both the memory and computational cost of self-attention by 4 to 8 times during training while also serving as a regularization method – improving generalization over dense training. We show that an ensemble of sub-models can be formed from the subsampled pathways within a network, which can achieve better performance than its densely attended counterpart. We perform experiments on a variety of NLP, computer vision and graph learning tasks in both generative and discriminative settings to provide empirical evidence for our claims and show the effectiveness of the proposed method.

  @inproceedings{2023-sigkdd,
      author = "Hussain, Md. Shamim and Zaki, Mohammed J. and Subramanian, Dharmashankar",
      title = "The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles",
      booktitle = "29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      year = "2023",
      month = "Aug",
      customlinkArxiv = "https://arxiv.org/abs/2306.01705",
      url = "https://dl.acm.org/doi/10.1145/3580305.3599520",
      customlinkgithubcode = "https://github.com/shamim-hussain/ssa",
      KeyWord = "Conference,Transformers,Attention,NeuralNetworks"
  }
  

• Fnu Mohbat, Mohammed J. Zaki, Catherine Finegan-Dollak, and Ashish Verma. Gvdoc - graph-based visual document classification. In Findings of the Association for Computational Linguistics, 5342–5357. 2023. URL: https://aclanthology.org/2023.findings-acl.329.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  The robustness of a model for real-world deployment is decided by how well it performs on unseen data and distinguishes between in-domain and out-of-domain samples. Visual document classifiers have shown impressive performance on in-distribution test sets. However, they tend to have a hard time correctly classifying and differentiating out-of-distribution examples. Image-based classifiers lack the text component, whereas multi-modality transformer-based models face the token serialization problem in visual documents due to their diverse layouts. They also require a lot of computing power during inference, making them impractical for many real-world applications. We propose, GVdoc, a graph-based document classification model that addresses both of these challenges. Our approach generates a document graph based on its layout, and then trains a graph neural network to learn node and graph embeddings. Through experiments, we show that our model, even with fewer parameters, outperforms state-of-the-art models on out-of-distribution data while retaining comparable performance on the in-distribution test set.

  @inproceedings{2023-acl,
      author = "Mohbat, Fnu and Zaki, Mohammed J. and Finegan{-}Dollak, Catherine and Verma, Ashish",
      title = "GVdoc - Graph-based Visual DOcument Classification",
      booktitle = "Findings of the Association for Computational Linguistics",
      pages = "5342--5357",
      year = "2023",
      url = "https://aclanthology.org/2023.findings-acl.329",
      customlinkgithubcode = "https://github.com/mohbattharani/GVdoc",
      KeyWord = "Conference"
  }
  

• Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki, and Parikshit Ram. End-to-end differentiable clustering with associative memories. In Proceedings of the 40th International Conference on Machine Learning. 2023.
  [abstract▼] [full text] [Arxiv] [githubcode] [BibTeX▼]

  Clustering is a widely used unsupervised learning technique involving an intensive discrete optimization problem. Associative Memory models or AMs are differentiable neural networks defining a recursive dynamical system, which have been integrated with various deep learning architectures. We uncover a novel connection between the AM dynamics and the inherent discrete assignment necessary in clustering to propose a novel unconstrained continuous relaxation of the discrete clustering problem, enabling end-to-end differentiable clustering with AM, dubbed ClAM. Leveraging the pattern completion ability of AMs, we further develop a novel self-supervised clustering loss. Our evaluations on varied datasets demonstrate that ClAM benefits from the self-supervision, and significantly improves upon both the traditional Lloyd's k-means algorithm, and more recent continuous clustering relaxations (by upto 60% in terms of the Silhouette Coefficient).

  @inproceedings{2023-icml,
      author = "Saha, Bishwajit and Krotov, Dmitry and Zaki, Mohammed J. and Ram, Parikshit",
      title = "End-to-end Differentiable Clustering with Associative Memories",
      booktitle = "Proceedings of the 40th International Conference on Machine Learning",
      year = "2023",
      customlinkArxiv = "http://arxiv.org/abs/2306.03209",
      customlinkgithubcode = "https://github.com/bsaha205/clam"
  }
  

• Benjamin Hoover, Yuchen Liang, Bao Pham, Rameswar Panda, Hendrik Strobelt, Duen Horng Chau, Mohammed J. Zaki, and Dmitry Krotov. Energy transformer. In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS). December 2023.
  [abstract▼] [full text] [Arxiv] [BibTeX▼]

  Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory. Attention is the power-house driving modern deep learning successes, but it lacks clear theoretical foundations. Energy-based models allow a principled approach to discriminative and generative tasks, but the design of the energy functional is not straightforward. At the same time, Dense Associative Memory models or Modern Hopfield Networks have a well-established theoretical foundation, and allow an intuitive design of the energy function. We propose a novel architecture, called the Energy Transformer (or ET for short), that uses a sequence of attention layers that are purposely designed to minimize a specifically engineered energy function, which is responsible for representing the relationships between the tokens. In this work, we introduce the theoretical foundations of ET, explore its empirical capabilities using the image completion task, and obtain strong quantitative results on the graph anomaly detection and graph classification tasks.

  @inproceedings{2023-ET,
      author = "Hoover, Benjamin and Liang, Yuchen and Pham, Bao and Panda, Rameswar and Strobelt, Hendrik and Chau, Duen Horng and Zaki, Mohammed J. and Krotov, Dmitry",
      title = "Energy Transformer",
      month = "December",
      year = "2023",
      booktitle = "Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS)",
      customlinkArxiv = "http://arxiv.org/abs/2302.07253"
  }
  

• Yu Chen, Lingfei Wu, and Mohammed J. Zaki. Toward subgraph-guided knowledge graph question generation with graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, April 2023. doi:https://doi.org/10.1109/TNNLS.2023.3264519.
  [abstract▼] [full text] [BibTeX▼]

  Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting that is to generate questions from a single KG triple. In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers. In addition, most previous works built on either RNN- or Transformer-based models to encode a linearized KG subgraph, which totally discards the explicit structure information of a KG subgraph. To address this issue, we propose to apply a bidirectional Graph2Seq model to encode the KG subgraph. Furthermore, we enhance our RNN decoder with a node-level copying mechanism to allow direct copying of node attributes from the KG subgraph to the output question. Both automatic and human evaluation results demonstrate that our model achieves new state-of-the-art scores, outperforming existing methods by a significant margin on two QG benchmarks. Experimental results also show that our QG model can consistently benefit the question-answering (QA) task as a means of data augmentation.

  @article{2023-tnnls,
      author = "Chen, Yu and Wu, Lingfei and Zaki, Mohammed J.",
      title = "Toward Subgraph-Guided Knowledge Graph Question Generation With Graph Neural Networks",
      journal = "IEEE Transactions on Neural Networks and Learning Systems",
      year = "2023",
      month = "April",
      doi = "https://doi.org/10.1109/TNNLS.2023.3264519",
      KeyWord = "Journal,GraphMining,NeuralNetworks,QA"
  }
  

• Aparna Gupta, Vipula Rawte, and Mohammed J. Zaki. Predicting firm financial performance from sec filing changes using automatically generated dictionary. Computational Economics, 2023. doi:https://doi.org/10.1007/s10614-023-10443-x.
  [abstract▼] [full text] [BibTeX▼]

  Textual data are increasingly used to predict firm performance, however extracting useful signals towards serving this goal with a continuously growing repository of financial reports and documents is challenging, even by the state-of-the-art machine learning and natural language processing (NLP) techniques. We propose a novel approach to automatically create a word list from SEC filings (10-K and 8-K reports) using advanced deep learning and NLP techniques and compare their performance against the widely used Loughran–McDonald sentiment dictionaries. We additionally analyze a corpus of 8-K and 10-K documents to evaluate their relative informativeness for firm performance prediction. Since 8-K filings provide corporate updates along a fiscal year, we compare their content against changes in 10-Ks between consecutive years to assess the incremental value of information provided in these regulatory filings. Information effectiveness is examined by predicting six key financial indicators for a set of US banks using ridge regression. Our results positively support sentiment dictionaries expansion by automatically extracting meaning from text and highlight the benefits obtainable from utilizing update filings.

  @article{2023-CE,
      author = "Gupta, Aparna and Rawte, Vipula and Zaki, Mohammed J.",
      title = "Predicting Firm Financial Performance from SEC Filing Changes Using Automatically Generated Dictionary",
      journal = "Computational Economics",
      year = "2023",
      doi = "https://doi.org/10.1007/s10614-023-10443-x"
  }
  

2022

• Muhammad Abulaish, Mohd Fazil, and Mohammed J. Zaki. Domain-specific keyword extraction using joint modeling of local and global contextual semantics. ACM Transactions on Knowledge Discovery from Data, 16(4):Article 70, January 2022. doi:10.1145/3494560.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  Domain-specific keyword extraction is a vital task in the field of text mining. There are various research tasks, such as spam e-mail classification, abusive language detection, sentiment analysis, and emotion mining, where a set of domain-specific keywords (aka lexicon) is highly effective. Existing works for keyword extraction list all keywords rather than domain-specific keywords from a document corpus. Moreover, most of the existing approaches perform well on formal document corpuses but fail on noisy and informal user generated content in online social media. In this article, we present a hybrid approach by jointly modeling the local and global contextual semantics of words, utilizing the strength of distributional word representation and contrasting-domain corpus for domain-specific keyword extraction. Starting with a seed set of a few domain-specific keywords, we model the text corpus as a weighted word-graph. In this graph, the initial weight of a node (word) represents its semantic association with the target domain calculated as a linear combination of three semantic association metrics, and the weight of an edge connecting a pair of nodes represents the co-occurrence count of the respective words. Thereafter, a modified PageRank method is applied to the word-graph to identify the most relevant words for expanding the initial set of domain-specific keywords. We evaluate our method over both formal and informal text corpuses (comprising six datasets), and show that it performs significantly better in comparison to state-of-the-art methods. Furthermore, we generalize our approach to handle the language-agnostic case, and show that it outperforms existing language-agnostic approaches.

  @article{2022-tkdd,
      author = "Abulaish, Muhammad and Fazil, Mohd and Zaki, Mohammed J.",
      title = "Domain-Specific Keyword Extraction Using Joint Modeling of Local and Global Contextual Semantics",
      journal = "ACM Transactions on Knowledge Discovery from Data",
      year = "2022",
      volume = "16",
      number = "4",
      DOI = "10.1145/3494560",
      month = "January",
      pages = "Article 70",
      customlinkgithubcode = "https://github.com/iammohdfazil/DSKE",
      keywords = "Text mining, information extraction, domain-specific keyword extraction, language-agnostic keyword extraction"
  }
  

• Md. Shamim Hussain, Mohammed J. Zaki, and Dharmashankar Subramanian. Global self-attention as a replacement for graph convolution. In 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Aug 2022. URL: https://dl.acm.org/doi/10.1145/3534678.3539296.
  [abstract▼] [full text] [Arxiv] [githubcode] [BibTeX▼]

  We propose an extension to the transformer neural network architecture for general-purpose graph learning by adding a dedicated pathway for pairwise structural information, called edge channels. The resultant framework -- which we call Edge-augmented Graph Transformer (EGT) -- can directly accept, process and output structural information of arbitrary form, which is important for effective learning on graph-structured data. Our model exclusively uses global self-attention as an aggregation mechanism rather than static localized convolutional aggregation. This allows for unconstrained long-range dynamic interactions between nodes. Moreover, the edge channels allow the structural information to evolve from layer to layer, and prediction tasks on edges/links can be performed directly from the output embeddings of these channels. We verify the performance of EGT in a wide range of graph-learning experiments on benchmark datasets, in which it outperforms Convolutional/Message-Passing Graph Neural Networks. EGT sets a new state-of-the-art for the quantum-chemical regression task on the OGB-LSC PCQM4Mv2 dataset containing 3.8 million molecular graphs. Our findings indicate that global self-attention based aggregation can serve as a flexible, adaptive and effective replacement of graph convolution for general-purpose graph learning. Therefore, convolutional local neighborhood aggregation is not an essential inductive bias.

  @inproceedings{2022-sigkdd,
      author = "Hussain, Md. Shamim and Zaki, Mohammed J. and Subramanian, Dharmashankar",
      title = "Global Self-Attention as a Replacement for Graph Convolution",
      booktitle = "28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      year = "2022",
      month = "Aug",
      customlinkArxiv = "https://arxiv.org/abs/2108.03348",
      url = "https://dl.acm.org/doi/10.1145/3534678.3539296",
      customlinkgithubcode = "https://github.com/shamim-hussain/egt",
      KeyWord = "Conference,GraphNN,NeuralNetworks"
  }
  

• Da Yan, Catia Pesquita, Carsten Goerg, Jake Chen, and Mohammed J. Zaki, editors. Proceedings of the 21st International Workshop on Data Mining in Bioinformatics, BIOKDD 2022, Washington, DC, USA, ACM, Aug 2022. URL: https://dl.acm.org/doi/10.1145/3534678.3542888.
  [full text] [BibTeX▼]

  @proceedings{2022-biokdd,
      editor = "Yan, Da and Pesquita, Catia and Goerg, Carsten and Chen, Jake and Zaki, Mohammed J.",
      title = "Proceedings of the 21st International Workshop on Data Mining in Bioinformatics, BIOKDD 2022, Washington, DC, USA",
      year = "2022",
      month = "Aug",
      publisher = "{ACM}",
      url = "https://dl.acm.org/doi/10.1145/3534678.3542888",
      KeyWord = "Book,Biokdd"
  }
  

• Yuchen Liang, Dmitry Krotov, and Mohammed J. Zaki. Associative learning for network embedding. In 8th International Workshop on Deep Learning on Graphs (DLG-KDD22). Aug 2022. URL: https://drive.google.com/file/d/1Esi_Nsv5VBDFN6kTXZ5Dgb_xU_U-OVuL/view?usp=sharing.
  [abstract▼] [full text] [BibTeX▼]

  The network embedding task is to represent the node in the network as a low-dimensional vector while incorporating the topological and structural information. Most existing approaches solve this problem by factorizing a proximity matrix, either directly or implicitly. In this work, we introduce a network embedding method from a new perspective, which leverages Modern Hopfield Networks (MHN) for associative learning. Our network learns associations between the content of each node and that node's neighbors. These associations serve as memories in the MHN. The recurrent dynamics of the network make it possible to recover the masked node, given that node's neighbors. Our proposed method is evaluated on different downstream tasks such as node classification and linkage prediction. The results show competitive performance compared to the common matrix factorization techniques and deep learning based methods.

  @inproceedings{2022-dlg,
      Author = "Liang, Yuchen and Krotov, Dmitry and Zaki, Mohammed J.",
      Title = "Associative Learning for Network Embedding",
      Year = "2022",
      month = "Aug",
      booktitle = "8th International Workshop on Deep Learning on Graphs (DLG-KDD22)",
      url = "https://drive.google.com/file/d/1Esi\_Nsv5VBDFN6kTXZ5Dgb\_xU\_U-OVuL/view?usp=sharing",
      KeyWord = "Workshop,GraphNN,AssociativeLearning"
  }
  

• Jonathan J. Harris and Mohammed J. Zaki. Towards neural numeric-to-text generation from temporal personal health data. In Workshop on Applied Data Science for Healthcare: Transparent and Human-centered AI (with KDD). Aug 2022.
  [abstract▼] [full text] [Arxiv] [githubcode] [BibTeX▼]

  With an increased interest in the production of personal health technologies designed to track user data (e.g., nutrient intake, step counts), there is now more opportunity than ever to surface meaningful behavioral insights to everyday users in the form of natural language. This knowledge can increase their behavioral awareness and allow them to take action to meet their health goals. It can also bridge the gap between the vast collection of personal health data and the summary generation required to describe an individual's behavioral tendencies. Previous work has focused on rule-based time-series data summarization methods designed to generate natural language summaries of interesting patterns found within temporal personal health data. We examine recurrent, convolutional, and Transformer-based encoder-decoder models to automatically generate natural language summaries from numeric temporal personal health data. We showcase the effectiveness of our models on real user health data logged in MyFitnessPal and show that we can automatically generate high-quality natural language summaries. Our work serves as a first step towards the ambitious goal of automatically generating novel and meaningful temporal summaries from personal health data.

  @inproceedings{2022-dshealth,
      author = "Harris, Jonathan J. and Zaki, Mohammed J.",
      title = "Towards Neural Numeric-To-Text Generation From Temporal Personal Health Data",
      booktitle = "Workshop on Applied Data Science for Healthcare: Transparent and Human-centered AI (with KDD)",
      year = "2022",
      month = "Aug",
      customlinkArxiv = "http://arxiv.org/abs/2207.05194",
      customlinkgithubcode = "https://github.com/harrij15/TemporalSummaries",
      KeyWord = "TemporalSummaries,DeepLearning,PersonalHealth"
  }
  

• Qitong Wang and Mohammed J. Zaki. Hg2vec: improved word embeddings from dictionary and thesaurus based heterogeneous graph. In Proceedings of the 29th International Conference on Computational Linguistics (COLING), 3154–3163. International Committee on Computational Linguistics, Oct 2022. URL: https://aclanthology.org/2022.coling-1.279.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  Learning word embeddings is an essential topic in natural language processing. Most existing works use a vast corpus as a primary source while training, but this requires massive time and space for data pre-processing and model training. We propose a new model, HG2Vec, that learns word embeddings utilizing only dictionaries and thesauri. Our model reaches the state-of-art on multiple word similarity and relatedness benchmarks. We demonstrate that dictionaries and thesauri are effective resources to learn word embeddings. In addition, we exploit a new context-focused loss that models transitive relationships between word pairs and balances the performance between similarity and relatedness benchmarks, yielding superior results.

  @inproceedings{2022-coling,
      author = "Wang, Qitong and Zaki, Mohammed J.",
      title = "HG2Vec: Improved Word Embeddings from Dictionary and Thesaurus Based Heterogeneous Graph",
      booktitle = "Proceedings of the 29th International Conference on Computational Linguistics (COLING)",
      pages = "3154--3163",
      publisher = "International Committee on Computational Linguistics",
      year = "2022",
      month = "Oct",
      url = "https://aclanthology.org/2022.coling-1.279",
      customlinkgithubcode = "https://github.com/Qitong-Wang/HG2Vec"
  }
  

• Diya Li and Mohammed J. Zaki. Food knowledge representation learning with adversarial substitution. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, AACL/IJCNLP - Volume 1: Long Papers, 653–664. Association for Computational Linguistics, Nov 2022. URL: https://aclanthology.org/2022.aacl-main.50.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  Knowledge graph embedding (KGE) has been well-studied in general domains, but has not been examined for food computing. To fill this gap, we perform knowledge representation learning over a food knowledge graph (KG). We employ a pre-trained language model to encode entities and relations, thus emphasizing contextual information in food KGs. The model is trained on two tasks – predicting a masked entity from a given triple from the KG and predicting the plausibility of a triple. Analysis of food substitutions helps in dietary choices for enabling healthier eating behaviors. Previous work in food substitutions mainly focuses on semantic similarity while ignoring the context. It is also hard to evaluate the substitutions due to the lack of an adequate validation set, and further, the evaluation is subjective based on perceived purpose. To tackle this problem, we propose a collection of adversarial sample generation strategies for different food substitutions over our learnt KGE. We propose multiple strategies to generate high quality context-aware recipe and ingredient substitutions and also provide generalized ingredient substitutions to meet different user needs. The effectiveness and efficiency of the proposed knowledge graph learning method and the following attack strategies are verified by extensive evaluations on a large-scale food KG.

  @inproceedings{2022-aacl,
      author = "Li, Diya and Zaki, Mohammed J.",
      title = "Food Knowledge Representation Learning with Adversarial Substitution",
      booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, {AACL/IJCNLP} - Volume 1: Long Papers",
      pages = "653--664",
      publisher = "Association for Computational Linguistics",
      year = "2022",
      month = "Nov",
      url = "https://aclanthology.org/2022.aacl-main.50",
      customlinkgithubcode = "https://github.com/DiyaLI916/FoodKGE"
  }
  

• Yuchen Liang, Dmitry Krotov, and Mohammed J. Zaki. Modern hopfield networks for graph embedding. Frontiers in Big Data, Special Issue on Distributed Representation Learning using Neural Network Embeddings, 5:1044709, Nov 2022. URL: https://www.frontiersin.org/articles/10.3389/fdata.2022.1044709, doi:https://doi.org/10.3389/fdata.2022.1044709.
  [abstract▼] [full text] [BibTeX▼]

  The network embedding task is to represent a node in a network as a low-dimensional vector while incorporating the topological and structural information. Most existing approaches solve this problem by factorizing a proximity matrix, either directly or implicitly. In this work, we introduce a network embedding method from a new perspective, which leverages Modern Hopfield Networks (MHN) for associative learning. Our network learns associations between the content of each node and that node's neighbors. These associations serve as memories in the MHN. The recurrent dynamics of the network make it possible to recover the masked node, given that node's neighbors. Our proposed method is evaluated on different benchmark datasets for downstream tasks such as node classification, link prediction, and graph coarsening. The results show competitive performance compared to the common matrix factorization techniques and deep learning based methods.

  @article{2022-frontiers,
      author = "Liang, Yuchen and Krotov, Dmitry and Zaki, Mohammed J.",
      title = "Modern Hopfield Networks for graph embedding",
      journal = "Frontiers in Big Data, Special Issue on Distributed Representation Learning using Neural Network Embeddings",
      volume = "5",
      pages = "1044709",
      year = "2022",
      month = "Nov",
      url = "https://www.frontiersin.org/articles/10.3389/fdata.2022.1044709",
      doi = "https://doi.org/10.3389/fdata.2022.1044709"
  }
  

• Ching-Hua Chen, Daniel Gruen, Jonathan Harris, James Hendler, Deborah L. McGuinness, Marco Monti, Nidhi Rastogi, Oshani Seneviratne, and Mohammed J. Zaki. Semantic technologies for clinically relevant personal health applications. In Personal Health Informatics: Patient Participation in Precision Health, Editors: Hsueh, Pei-Yun Sabrina and Wetter, Thomas and Zhu, Xinxin, 199–220. Cham, 2022. Springer International Publishing. URL: https://doi.org/10.1007/978-3-031-07696-1_10, doi:10.1007/978-3-031-07696-1_10.
  [abstract▼] [full text] [BibTeX▼]

  Despite recent advances in digital health solutions and machine learning, personal health applications that aim to modify health behaviors are still limited in their ability to offer more personalized decision support. Moreover, while many personal health applications cater to general health and well-being, there remains a significant opportunity to increase the clinical relevance of the insights being generated. This chapter describes the motivation for, and illustrative applications of, semantic technologies for enabling clinically relevant personal health applications. We present two use cases that demonstrate how semantic web technologies, in combination with machine learning and data mining methods, can be used to provide personalized insights to support behaviors that are consistent with nutritional guidelines for people with diabetes.

  @inproceedings{2022-springer,
      author = "Chen, Ching-Hua and Gruen, Daniel and Harris, Jonathan and Hendler, James and McGuinness, Deborah L. and Monti, Marco and Rastogi, Nidhi and Seneviratne, Oshani and Zaki, Mohammed J.",
      title = "Semantic Technologies for Clinically Relevant Personal Health Applications",
      booktitle = "Personal Health Informatics: Patient Participation in Precision Health, Editors: Hsueh, Pei-Yun Sabrina and Wetter, Thomas and Zhu, Xinxin",
      year = "2022",
      publisher = "Springer International Publishing",
      address = "Cham",
      pages = "199--220",
      doi = "10.1007/978-3-031-07696-1\_10",
      url = "https://doi.org/10.1007/978-3-031-07696-1\_10"
  }
  

• Nidhi Rastogi, Sharmishtha Dutta, Alex Gittens, Mohammed J. Zaki, and Charu C. Aggarwal. TINKER: A framework for open source cyberthreat intelligence. In IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2022), Wuhan, China, 1569–1574. December 2022. URL: https://ieeexplore.ieee.org/document/10063681, doi:10.1109/TrustCom56396.2022.00225.
  [abstract▼] [full text] [BibTeX▼]

  Threat intelligence on malware attacks and campaigns is increasingly being shared with other security experts for a cost or for free. Other security analysts use this intelligence to inform them of indicators of compromise, attack techniques, and preventative actions. Security analysts prepare threat analysis reports after investigating an attack, an emerging cyber threat, or a recently discovered vulnerability. Collectively known as cyber threat intelligence (CTI), the reports are typically in an unstructured format and, therefore, challenging to integrate seamlessly into existing intrusion detection systems. This paper proposes a framework that uses the aggregated CTI for analysis and defense at scale. The information is extracted and stored in a structured format using knowledge graphs such that the semantics of the threat intelligence can be preserved and shared at scale with other security analysts. Specifically, we propose the first semi-supervised open-source knowledge graph-based framework, TINKER, to capture cyber threat information and its context. Following TINKER, we generate a Cyberthreat Intelligence Knowledge Graph (CTI-KG) and demonstrate the usage using different use cases.

  @inproceedings{2022-trustcom,
      author = "Rastogi, Nidhi and Dutta, Sharmishtha and Gittens, Alex and Zaki, Mohammed J. and Aggarwal, Charu C.",
      title = "{TINKER:} {A} framework for Open source Cyberthreat Intelligence",
      booktitle = "{IEEE} International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2022), Wuhan, China",
      month = "December",
      year = "2022",
      pages = "1569--1574",
      doi = "10.1109/TrustCom56396.2022.00225",
      url = "https://ieeexplore.ieee.org/document/10063681"
  }
  

2021

• Vipula Rawte, Aparna Gupta, and Mohammed J. Zaki. A comparative analysis of temporal long text similarity: application to financial documents. In V. Bitetta, I. Bordino, A. Ferretti, F. Gullo, G. Ponti, and L. Severini, editors, Mining Data for Financial Applications: Fifth Workshop on MIning DAta for financial applicationS (with ECML-PKDD); Revised Selected Papers, volume 12591 of LNCS. Springer, Cham., January 2021. URL: https://link.springer.com/chapter/10.1007/978-3-030-66981-2_7, doi:https://doi.org/10.1007/978-3-030-66981-2_7.
  [full text] [BibTeX▼]

  @incollection{2020-midas,
      author = "Rawte, Vipula and Gupta, Aparna and Zaki, Mohammed J.",
      editor = "Bitetta, V. and Bordino, I. and Ferretti, A. and Gullo, F. and Ponti, G. and Severini, L.",
      title = "A comparative analysis of Temporal Long Text Similarity: Application to Financial Documents",
      booktitle = "Mining Data for Financial Applications: Fifth Workshop on MIning DAta for financial applicationS (with ECML-PKDD); Revised Selected Papers",
      series = "LNCS",
      volume = "12591",
      year = "2021",
      publisher = "Springer, Cham.",
      month = "January",
      url = "https://link.springer.com/chapter/10.1007/978-3-030-66981-2\_7",
      DOI = "https://doi.org/10.1007/978-3-030-66981-2\_7",
      keywords = "Workshop,Chapter,TextMining"
  }
  

• Yu Chen, Ananya Subburathinam, Ching-Hua Chen, and Mohammed J. Zaki. Personalized food recommendation as constrained question answering over a large-scale food knowledge graph. In Fourteenth ACM International Conference on Web Search and Data Mining (WSDM). Mar 2021.
  [abstract▼] [full text] [Arxiv] [githubcode] [BibTeX▼]

  Food recommendation has become an important means to help guide users to adopt healthy dietary habits. Previous works on food recommendation either i) fail to consider users' explicit requirements, ii) ignore crucial health factors (e.g., allergies and nutrition needs), or iii) do not utilize the rich food knowledge for recommending healthy recipes. To address these limitations, we propose a novel problem formulation for food recommendation, modeling this task as constrained question answering over a large-scale food knowledge base/graph (KBQA). Besides the requirements from the user query, personalized requirements from the user's dietary preferences and health guidelines are handled in a unified way as additional constraints to the QA system. To validate this idea, we create a QA style dataset for personalized food recommendation based on a large-scale food knowledge graph and health guidelines. Furthermore, we propose a KBQA-based personalized food recommendation framework which is equipped with novel techniques for handling negations and numerical comparisons in the queries. Experimental results on the benchmark show that our approach significantly outperforms non-personalized counterparts (average 59.7\% absolute improvement across various evaluation metrics), and is able to recommend more relevant and healthier recipes.

  @inproceedings{2021-wsdm,
      author = "Chen, Yu and Subburathinam, Ananya and Chen, Ching-Hua and Zaki, Mohammed J.",
      title = "Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph",
      booktitle = "Fourteenth ACM International Conference on Web Search and Data Mining (WSDM)",
      year = "2021",
      month = "Mar",
      customlinkArxiv = "https://arxiv.org/abs/2101.01775",
      customlinkgithubcode = "https://github.com/hugochan/PFoodReq",
      KeyWord = "Conference,NeuralNetworks,KBQA,PersonalHealth,FoodRecommendation"
  }
  

• Yuchen Liang, Chaitanya K. Ryali, Benjamin Hoover, Leopold Grinberg, Saket Navlakha, Mohammed J. Zaki, and Dmitry Krotov. Can a fruit fly learn word embeddings. In International Conference on Learning Representations (ICLR). May 2021.
  [abstract▼] [Arxiv] [githubcode] [BibTeX▼]

  The mushroom body of the fruit fly brain is one of the best studied systems in neuroscience. At its core it consists of a population of Kenyon cells, which receive inputs from multiple sensory modalities. These cells are inhibited by the anterior paired lateral neuron, thus creating a sparse high dimensional representation of the inputs. In this work we study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language processing (NLP) task. We show that this network can learn semantic representations of words and can generate both static and context-dependent word embeddings. Unlike conventional methods (e.g., BERT, GloVe) that use dense representations for word embedding, our algorithm encodes semantic meaning of words and their context in the form of sparse binary hash codes. The quality of the learned representations is evaluated on word similarity analysis, word-sense disambiguation, and document classification. It is shown that not only can the fruit fly network motif achieve performance comparable to existing methods in NLP, but, additionally, it uses only a fraction of the computational resources (shorter training time and smaller memory footprint).

  @inproceedings{2021-iclr,
      author = "Liang, Yuchen and Ryali, Chaitanya K. and Hoover, Benjamin and Grinberg, Leopold and Navlakha, Saket and Zaki, Mohammed J. and Krotov, Dmitry",
      title = "Can a Fruit Fly Learn Word Embeddings",
      booktitle = "International Conference on Learning Representations (ICLR)",
      year = "2021",
      month = "May",
      customlinkArxiv = "https://arxiv.org/abs/2101.06887",
      customlinkgithubcode = "https://github.com/bhoov/flyvec",
      KeyWord = "Conference,NeuralNetworks,WordEmbeddings"
  }
  

• Jonathan J. Harris, Ching-Hua Chen, and Mohammed J. Zaki. A framework for generating summaries from temporal personal health data. ACM Transactions on Computing for Healthcare, 2(3):Article 21, 2021. doi:https://dl.acm.org/doi/abs/10.1145/3448672.
  [abstract▼] [full text] [Arxiv] [githubcode] [BibTeX▼]

  Although it has become easier for individuals to track their personal health data (e.g., heart rate, step count, and nutrient intake data), there is still a wide chasm between the collection of data and the generation of meaningful summaries to help users better understand what their data means to them. With an increased comprehension of their data, users will be able to act upon the newfound information and work toward striving closer to their health goals. We aim to bridge the gap between data collection and summary generation by mining the data for interesting behavioral findings that may provide hints about a user’s tendencies. Our focus is on improving the explainability of temporal personal health data via a set of informative summary templates, or ``protoforms.'' These protoforms span both evaluation-based summaries that help users evaluate their health goals and pattern-based summaries that explain their implicit behaviors. In addition to individual-level summaries, the protoforms we use are also designed for population-level summaries. We apply our approach to generate summaries (both univariate and multivariate) from real user health data and show that the summaries our system generates are both interesting and useful.

  @article{2021-thealth,
      author = "Harris, Jonathan J. and Chen, Ching{-}Hua and Zaki, Mohammed J.",
      title = "A Framework for Generating Summaries from Temporal Personal Health Data",
      journal = "ACM Transactions on Computing for Healthcare",
      volume = "2",
      number = "3",
      pages = "Article 21",
      year = "2021",
      customlinkArxiv = "https://arxiv.org/abs/2003.09530",
      DOI = "https://dl.acm.org/doi/abs/10.1145/3448672",
      customlinkgithubcode = "https://github.com/harrij15/TemporalSummaries",
      KeyWord = "TemporalSummaries,SequenceMining,PersonalHealth"
  }
  

• Diya Li, Mohammed J. Zaki, and Ching-Hua Chen. Nutrition guided recipe search via pre-trained recipe embeddings. In IEEE Workshop on Data Engineering Meets intelligent food and Cooking Recipes (DECOR), with IEEE ICDE Conference. Apr 2021. doi:10.1109/ICDEW53142.2021.00011.
  [abstract▼] [full text] [BibTeX▼]

  The use of machine learning to recommend foods that are both healthy and tasty is an open problem. Fundamentally, it is challenging to balance health goals with preferences in taste, while offering users a large diversity of options. Representing recipes via embedding vectors trained on large-scale food datasets can capture the implicit semantics of a recipe. We utilize pre-trained embeddings to perform recipe search and compare our search results with a keyword based search. We compare the health score, nutritional content and recipe titles returned using both search approaches. Our exploratory experiments show that recipe search via embeddings can return more diverse recipe titles in contrast to keyword based search.

  @inproceedings{2021-decor,
      author = "Li, Diya and Zaki, Mohammed J. and Chen, Ching-Hua",
      title = "Nutrition Guided Recipe Search via Pre-trained Recipe Embeddings",
      booktitle = "IEEE Workshop on Data Engineering Meets intelligent food and Cooking Recipes (DECOR), with IEEE ICDE Conference",
      year = "2021",
      month = "Apr",
      DOI = "10.1109/ICDEW53142.2021.00011",
      KeyWord = "Workshop,FoodRecommendation,PersonalHealth"
  }
  

• Eman Maghawry, Tarek F. Gharib, Rasha Ismail, and Mohammed J. Zaki. An efficient heartbeats classifier based on optimizing convolutional neural network model. IEEE Access, 9:153266–153275, Nov 2021. doi:https://doi.org/10.1109/ACCESS.2021.3128134.
  [abstract▼] [full text] [BibTeX▼]

  Recently, deep learning models have emerged as promising methods for the diagnosis of different diseases. Cardiac disease is among the leading life-threatening diseases on a global scale. The aim of this paper is to propose an optimized Convolutional Neural Network (CNN) model for the classification of electrocardiogram (ECG) heartbeat data. The proposed ECG classification approach is designed with an optimal CNN configuration to classify cardiac arrhythmias quickly and effectively. Finding an optimal configuration for the CNN hyperparameters is time-consuming and needs extensive experimentation. To overcome this challenge, we present an optimization step for the proposed CNN model using a customized genetic algorithm. It provides an automatic suggestion for the best hyperparameter settings of the proposed CNN. The challenge in utilizing the genetic algorithm is that its operators need to be customized to handle our problem domain. Our approach accepts raw ECG signals without any preprocessing steps, which has benefit in saving the computation time. Our approach also provides a resampling step to ensure generalization, to better handle imbalanced ECG classes. Experiments show promising results of our proposed approach against other approaches whose CNN hyperparameters setting depended on numerous trials, requiring extensive ECG feature extraction steps, and do not consider imbalanced classes. The performance of our proposed approach is better than other existing methods both in terms of higher classification accuracy (98.45\%), and lower computational complexity.

  @article{2021-IEEEaccess,
      author = "Maghawry, Eman and Gharib, Tarek F. and Ismail, Rasha and Zaki, Mohammed J.",
      title = "An Efficient Heartbeats Classifier Based on Optimizing Convolutional Neural Network Model",
      journal = "IEEE Access",
      volume = "9",
      pages = "153266--153275",
      year = "2021",
      month = "Nov",
      DOI = "https://doi.org/10.1109/ACCESS.2021.3128134",
      KeyWord = "Journal,NeuralNetworks"
  }
  

• Bowen Wang, Zehai Wang, Atharva A. Poundarik, Mohammed J. Zaki, Richard S. Bockman, Benjamin S. Glicksberg, Girish N. Nadkarni, and Deepak Vashishth. Unmasking fracture risk in type 2 diabetes: the association of longitudinal glycemic hemoglobin level and medications. The Journal of Clinical Endocrinology & Metabolism, Dec 2021. doi:https://doi.org/10.1210/clinem/dgab882.
  [abstract▼] [full text] [BibTeX▼]

  Context: Fracture risk is underestimated in people with type 2 diabetes (T2D). Objective: To investigate the longitudinal relationship of glycated hemoglobin (HbA1c) and common medications on fracture risk in people with T2D. Design: Retrospective cohort study was conducted using de-identified claims and EHR data obtained from the OptumLabs ® Data Warehouse during 01/01/2007 to 09/30/2015. For each individual, the study was conducted within a two-year HbA1c observation period and a two-year fracture follow-up period. Setting: Population-based study. Participants: 157,439 individuals with T2D [age ≥ 55 years with mean HbA1c value ≥ 6%] were selected from 4,018,250 US Medicare Advantage/Commercial enrollee with T2D diagnosis. Main Outcome Measures. All fractures and fragility fractures were measured. Results: With covariates adjusted, poor glycemic control in T2D individuals was associated with an 29% increase of all fracture risk, compared to T2D individuals with adequate glycemic control (HR: 1.29, 95% CI 1.22-1.36). Treatment with metformin (HR: 0.88, 95% CI 0.85-0.92) and DPP4 inhibitors (HR: 0.93, 95% CI 0.88-0.98) were associated with a reduced all fracture risk, while insulin (HR: 1.26, 95% CI 1.21-1.32), thiazolidinediones (HR: 1.23, 95% CI 1.18-1.29), meglitinides (HR: 1.12, 95% CI 1.00-1.26) were associated with an increased all fracture risk (All p-value < 0.05). Bisphosphonates were associated similarly with increased fracture risk in T2D group and in non-diabetic group. Conclusions: Longitudinal two-year HbA1c is independently associated with an elevated all fracture risk in T2D individuals during a two-year follow-up period. Metformin and DPP4 inhibitors can be used for management of T2D fracture risk

  @article{2021-JCEM,
      author = "Wang, Bowen and Wang, Zehai and Poundarik, Atharva A. and Zaki, Mohammed J. and Bockman, Richard S. and Glicksberg, Benjamin S. and Nadkarni, Girish N. and Vashishth, Deepak",
      title = "Unmasking Fracture Risk in Type 2 Diabetes: The Association of Longitudinal Glycemic Hemoglobin Level and Medications",
      journal = "The Journal of Clinical Endocrinology \\& Metabolism",
      number = "dgab882",
      year = "2021",
      month = "Dec",
      DOI = "https://doi.org/10.1210/clinem/dgab882",
      KeyWord = "Journal"
  }
  

• Yuchen Liang and Mohammed J. Zaki. Keyphrase extraction using neighborhood knowledge based on word embeddings. arXiv Computing Research Repository, 2021. URL: http://arxiv.org/abs/2111.07198.
  [abstract▼] [full text] [BibTeX▼]

  Keyphrase extraction is the task of finding several interesting phrases in a text document, which provide a list of the main topics within the document. Most existing graph-based models use co-occurrence links as cohesion indicators to model the relationship of syntactic elements. However, a word may have different forms of expression within the document, and may have several synonyms as well. Simply using co-occurrence information cannot capture this information. In this paper, we enhance the graph-based ranking model by leveraging word embeddings as background knowledge to add semantic information to the inter-word graph. Our approach is evaluated on established benchmark datasets and empirical results show that the word embedding neighborhood information improves the model performance.

  @article{2021-arxiv-keyphrase,
      Author = "Liang, Yuchen and Zaki, Mohammed J.",
      Title = "Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings",
      Year = "2021",
      journal = "arXiv Computing Research Repository",
      volume = "abs:2111.07198",
      url = "http://arxiv.org/abs/2111.07198",
      KeyWord = "TechReport,WordEmbeddings"
  }
  

2020

• Yu Chen, Lingfei Wu, and Mohammed J. Zaki. Deep iterative and adaptive learning for graph neural networks. In The First International Workshop on Deep Learning on Graphs: Methodologies and Applications (with AAAI). February 2020. *Best Student Paper Award*. URL: https://deep-learning-graphs.bitbucket.io/dlg-aaai20/.
  [abstract▼] [full text] [Arxiv] [BibTeX▼]

  In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously. We first cast the graph structure learning problem as a similarity metric learning problem and leverage an adapted graph regularization for controlling smoothness, connectivity and sparsity of the generated graph. We further propose a novel iterative method for searching for a hidden graph structure that augments the initial graph structure. Our iterative method dynamically stops when the learned graph structure approaches close enough to the optimal graph. Our extensive experiments demonstrate that the proposed DIAL-GNN model can consistently outperform or match state-of-the-art baselines in terms of both downstream task performance and computational time. The proposed approach can cope with both transductive learning and inductive learning.

  @inproceedings{2020-dlgma,
      author = "Chen, Yu and Wu, Lingfei and Zaki, Mohammed J.",
      title = "Deep Iterative and Adaptive Learning for Graph Neural Networks",
      booktitle = "The First International Workshop on Deep Learning on Graphs: Methodologies and Applications (with AAAI)",
      year = "2020",
      month = "February",
      Note = "*Best Student Paper Award*",
      url = "https://deep-learning-graphs.bitbucket.io/dlg-aaai20/",
      customlinkArxiv = "https://arxiv.org/abs/1912.07832",
      KeyWord = "Conference,GraphMining,NeuralNetworks"
  }
  

• Muhammad Abulaish, Ashraf Kamal, and Mohammed J. Zaki. A survey of figurative language and its computational detection in online social networks. ACM Transactions on the Web, January 2020. doi:10.1145/3375547.
  [abstract▼] [full text] [Kudos] [BibTeX▼]

  The frequent usage of figurative language on online social networks, especially on Twitter, has the potential to mislead traditional sentiment analysis and recommender systems. Due to the extensive use of slangs, bashes, flames, and non-literal texts, tweets are a great source of figurative language, such as sarcasm, irony, metaphor, simile, hyperbole, humor, and satire. Starting with a brief introduction of figurative language and its various categories, this article presents an in-depth survey of the state-of-the-art techniques for computational detection of seven different figurative language categories, mainly on Twitter. For each figurative language category, we present details about the characterizing features, datasets, and state-of-the-art computational detection approaches. Finally, we discuss open challenges and future directions of research for each figurative language category.

  @article{2020-tweb,
      author = "Abulaish, Muhammad and Kamal, Ashraf and Zaki, Mohammed J.",
      title = "A Survey of Figurative Language and Its Computational Detection in Online Social Networks",
      year = "2020",
      issue_date = "February 2020",
      publisher = "Association for Computing Machinery",
      address = "New York, NY, USA",
      volume = "14",
      number = "1",
      issn = "1559-1131",
      doi = "10.1145/3375547",
      journal = "ACM Transactions on the Web",
      month = "January",
      articleno = "Article 3",
      numpages = "52",
      customlinkKudos = "http://www.cs.rpi.edu/\textasciitilde zaki/PaperDir/KudosLink-TWEB20.pdf",
      keywords = "hyperbole detection, irony detection, figurative language, satire detection, metaphor detection, simile detection, sarcasm detection, humor recognition, Social network analysis"
  }
  

• Yu Chen, Lingfei Wu, and Mohammed J. Zaki. Reinforcement learning based graph-to-sequence model for natural question generation. In International Conference on Learning Representations (ICLR). April 2020. URL: https://arxiv.org/abs/1908.04942.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  Natural question generation (QG) aims to generate questions from a passage and an answer. Previous works on QG either (i) ignore the rich structure information hidden in text, (ii) solely rely on cross-entropy loss that leads to issues like exposure bias and inconsistency between train/test measurement, or (iii) fail to fully exploit the answer information. To address these limitations, in this paper, we propose a reinforcement learning (RL) based graph-to-sequence (Graph2Seq) model for QG. Our model consists of a Graph2Seq generator with a novel Bidirectional Gated Graph Neural Network based encoder to embed the passage, and a hybrid evaluator with a mixed objective combining both cross-entropy and RL losses to ensure the generation of syntactically and semantically valid text. We also introduce an effective Deep Alignment Network for incorporating the answer information into the passage at both the word and contextual levels. Our model is end-to-end trainable and achieves new state-of-the-art scores, outperforming existing methods by a significant margin on the standard SQuAD benchmark.

  @inproceedings{2020-iclr,
      author = "Chen, Yu and Wu, Lingfei and Zaki, Mohammed J.",
      title = "Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation",
      booktitle = "International Conference on Learning Representations (ICLR)",
      year = "2020",
      month = "April",
      url = "https://arxiv.org/abs/1908.04942",
      customlinkgithubcode = "https://github.com/zakimjz/RL-based-Graph2Seq-for-NQG",
      KeyWord = "Conference,GraphMining,NeuralNetworks,QA"
  }
  

• Nidhi Rastogi and Mohammed J. Zaki. Personal health knowledge graphs for patients. In Workshop on the Personal Health Knowledge Graph (with the Knowledge Graph Conference). May 2020.
  [full text] [BibTeX▼]

  @inproceedings{2020-phkg,
      author = "Rastogi, Nidhi and Zaki, Mohammed J.",
      title = "Personal Health Knowledge Graphs for Patients",
      booktitle = "Workshop on the Personal Health Knowledge Graph (with the Knowledge Graph Conference)",
      year = "2020",
      month = "May",
      KeyWord = "Workshop,KnowledgeGraphs,PersonalHealth"
  }
  

• Yu Chen, Lingfei Wu, and Mohammed J. Zaki. GraphFlow: exploiting conversation flow with graph neural networks for conversational machine comprehension. In International Joint Conference on Artificial Intelligence (IJCAI). July 2020. URL: https://arxiv.org/abs/1908.00059.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  Conversational machine comprehension (MC) has proven significantly more challenging compared to traditional MC since it requires better utilization of conversation history. However, most existing approaches do not effectively capture conversation history and thus have trouble handling questions involving coreference or ellipsis. Moreover, when reasoning over passage text, most of them simply treat it as a word sequence without exploring rich semantic relationships among words. In this paper, we first propose a simple yet effective graph structure learning technique to dynamically construct a question and conversation history aware context graph at each conversation turn. Then we propose a novel Recurrent Graph Neural Network, and based on that, we introduce a flow mechanism to model the temporal dependencies in a sequence of context graphs. The proposed GRAPH FLOW model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks. In addition, visualization experiments show that our proposed model can offer good interpretability for the reasoning process.

  @inproceedings{2020-ijcai,
      author = "Chen, Yu and Wu, Lingfei and Zaki, Mohammed J.",
      title = "{GraphFlow}: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension",
      booktitle = "International Joint Conference on Artificial Intelligence (IJCAI)",
      year = "2020",
      month = "July",
      url = "https://arxiv.org/abs/1908.00059",
      customlinkgithubcode = "https://github.com/zakimjz/GraphFlow",
      KeyWord = "Conference,GraphMining,NeuralNetworks,QA"
  }
  

• Vipula Rawte, Aparna Gupta, and Mohammed J. Zaki. Hierarchical contextual document embeddings for long financial text regression. In Proceedings of the 3rd Workshop KDD Workshop on Machine Learning in Finance (with SIGKDD). August 2020.
  [full text] [BibTeX▼]

  @inproceedings{2020-mlfinance,
      author = "Rawte, Vipula and Gupta, Aparna and Zaki, Mohammed J.",
      title = "Hierarchical Contextual Document Embeddings for Long Financial Text Regression",
      booktitle = "Proceedings of the 3rd Workshop KDD Workshop on Machine Learning in Finance (with SIGKDD)",
      year = "2020",
      month = "August",
      keywords = "Workshop,TextMining"
  }
  

• Yu Chen, Ching-Hua Chen, and Mohammed J. Zaki. Combining user preferences and health needs in personalized food recommendation. In Proceedings of the American Medical Informatics Association (AMIA) Virtual Annual Symposium, AMIA'20. November 2020.
  [full text] [BibTeX▼]

  @inproceedings{2020-amia,
      author = "Chen, Yu and Chen, Ching-Hua and Zaki, Mohammed J.",
      title = "Combining User Preferences and Health Needs in Personalized Food Recommendation",
      booktitle = "Proceedings of the American Medical Informatics Association (AMIA) Virtual Annual Symposium",
      series = "AMIA'20",
      year = "2020",
      month = "November",
      keywords = "Abstract,PersonalHealth,KBQA"
  }
  

• Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens, and Charu Aggarwal. MALOnt: an ontology for malware threat intelligence. In Proceedings of MLHat: The First International Workshop on Deployable Machine Learning for Security Defense (with SIGKDD). August 2020.
  [full text] [BibTeX▼]

  @inproceedings{2020-mlhat,
      author = "Rastogi, Nidhi and Dutta, Sharmishtha and Zaki, Mohammed J. and Gittens, Alex and Aggarwal, Charu",
      title = "{MALOnt}: An Ontology for Malware Threat Intelligence",
      booktitle = "Proceedings of MLHat: The First International Workshop on Deployable Machine Learning for Security Defense (with SIGKDD)",
      month = "August",
      year = "2020",
      keywords = "Workshop,Security,Ontology,KnowledgeGraphs"
  }
  

• Diya Li and Mohammed J. Zaki. RECIPTOR: an effective pretrained model for recipe representation learning. In ACM SIGKDD International Conference on Data Mining and Knowledge Discovery. Aug 2020. doi:10.1145/3394486.3403223.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  Recipe representation plays an important role in food computing for perception, recognition, recommendation and other applications. Learning pretrained recipe embeddings is a challenging task, as there is a lack of high quality annotated food datasets. In this paper, we provide a joint approach for learning effective pretrained recipe embeddings using both the ingredients and cooking instructions. We present Reciptor, a novel set transformer-based joint model to learn recipe representations, that preserves permutation-invariance for the ingredient set and uses a novel knowledge graph (KG) derived triplet sampling approach to optimize the learned embeddings so that related recipes are closer in the latent semantic space. The embeddings are further jointly optimized by combining similarity among cooking instructions with a KG based triplet loss. We experimentally show that Reciptor’s recipe embeddings outperform state-of-the-art baselines on two newly designed downstream classification tasks by a wide margin.

  @inproceedings{2020-kdd,
      author = "Li, Diya and Zaki, Mohammed J.",
      title = "{RECIPTOR}: An Effective Pretrained Model for Recipe Representation Learning",
      booktitle = "ACM SIGKDD International Conference on Data Mining and Knowledge Discovery",
      DOI = "10.1145/3394486.3403223",
      year = "2020",
      month = "Aug",
      customlinkgithubcode = "https://github.com/zakimjz/Reciptor",
      KeyWord = "Conference,NeuralNetworks,PersonalHealth,FoodRecommendation"
  }
  

• Yu Chen, Lingfei Wu, and Mohammed J. Zaki. Iterative deep graph learning for graph neural networks: better and robust node embeddings. In Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS). Dec 2020.
  [abstract▼] [full text] [githubcode] [BibTeX▼]

  In this paper, we propose an end-to-end graph learning framework, namely I terative D eep G raph L earning (IDGL), for jointly and iteratively learning graph structure and graph embedding. The key rationale of IDGL is to learn a better graph structure based on better node embeddings, and vice versa (i.e., better node embeddings based on a better graph structure). Our iterative method dynamically stops when the learned graph structure approaches close enough to the graph optimized for the downstream prediction task. In addition, we cast the graph learning problem as a similarity metric learning problem and leverage adaptive graph regularization for controlling the quality of the learned graph. Finally, combining the anchor- based approximation technique, we further propose a scalable version of IDGL, namely IDGL-A NCH , whichsignificantlyreducesthetimeandspacecomplexityof IDGL without compromising the performance. Our extensive experiments on nine benchmarks show that our proposed IDGL models can consistently outperform or match the state-of-the-art baselines. Furthermore, IDGL can be more robust to adversarial graphs and cope with both transductive and inductive learning.

  @inproceedings{2020-neurips,
      author = "Chen, Yu and Wu, Lingfei and Zaki, Mohammed J.",
      title = "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings",
      booktitle = "Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS)",
      year = "2020",
      month = "Dec",
      customlinkgithubcode = "https://github.com/hugochan/IDGL",
      KeyWord = "Conference,NeuralNetworks,GraphNN"
  }
  

2019

• Rodrigo L. Cardoso, Wagner Meira Jr, Virgilio Almeida, and Mohammed J. Zaki. A framework for benchmarking discrimination-aware models in machine learning. In AAAI/ACM Conference on AI, Ethics, and Society. January 2019.
  [full text] [BibTeX▼]

  @inproceedings{2019-aies,
      author = "Cardoso, Rodrigo L. and Jr, Wagner Meira and Almeida, Virgilio and Zaki, Mohammed J.",
      title = "A framework for benchmarking discrimination-aware models in machine learning",
      booktitle = "AAAI/ACM Conference on AI, Ethics, and Society",
      year = "2019",
      month = "January",
      KeyWord = "Conference,DiscriminationAware,Ethics"
  }
  

• Yu Chen, Lingfei Wu, and Mohammed J. Zaki. Bidirectional attentive memory networks for question answering over knowledge bases. In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). June 2019.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2019-naacl,
      author = "Chen, Yu and Wu, Lingfei and Zaki, Mohammed J.",
      title = "Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases",
      booktitle = "Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)",
      year = "2019",
      month = "June",
      customlinkgithubcode = "https://github.com/zakimjz/BAMnet",
      KeyWord = "Conference,KBQA,QA,NeuralNetworks"
  }
  

• Yu Chen, Lingfei Wu, and Mohammed J. Zaki. Graphflow: exploiting conversation flow with graph neural networks for conversational machine comprehension. In ICML Workshop on Learning and Reasoning with Graph-Structured Representations. June 2019. URL: https://graphreason.github.io/papers.
  [full text] [BibTeX▼]

  @inproceedings{2019-graphreason,
      author = "Chen, Yu and Wu, Lingfei and Zaki, Mohammed J.",
      title = "GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension",
      booktitle = "ICML Workshop on Learning and Reasoning with Graph-Structured Representations",
      year = "2019",
      month = "June",
      url = "https://graphreason.github.io/papers",
      KeyWord = "Conference,MRC,QA,NeuralNetworks"
  }
  

• Steven Haussmann, Oshani Seneviratne, Yu Chen, Yarden Ne'eman, James Codella, Ching-Hua Chen, Deborah L. McGuinness, and Mohammed J. Zaki. Foodkg: a semantics-driven knowledge graph for food recommendation. In International Semantic Web Conference (Resources Track Full Paper). October 2019.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2019-iswc,
      author = "Haussmann, Steven and Seneviratne, Oshani and Chen, Yu and Ne'eman, Yarden and Codella, James and Chen, Ching-Hua and McGuinness, Deborah L. and Zaki, Mohammed J.",
      title = "FoodKG: A Semantics-Driven Knowledge Graph for Food Recommendation",
      booktitle = "International Semantic Web Conference (Resources Track Full Paper)",
      month = "October",
      year = "2019",
      customlinkgithubcode = "https://foodkg.github.io/",
      KeyWord = "Conference,KnowledgeGraphs,KBQA,FoodRecommendation"
  }
  

• Steven Haussmann, Yu Chen, Oshani Seneviratne, Nidhi Rastogi, James Codella, Ching-Hua Chen, Deborah L. McGuinness, and Mohammed J. Zaki. Foodkg enabled Q&A application. In International Semantic Web Conference (Demo Track). October 2019.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2019-iswc-demo,
      author = "Haussmann, Steven and Chen, Yu and Seneviratne, Oshani and Rastogi, Nidhi and Codella, James and Chen, Ching-Hua and McGuinness, Deborah L. and Zaki, Mohammed J.",
      title = "FoodKG Enabled {Q\\&A} Application",
      booktitle = "International Semantic Web Conference (Demo Track)",
      month = "October",
      year = "2019",
      customlinkgithubcode = "https://foodkg.github.io/",
      KeyWord = "Conference,KnowledgeGraphs,KBQA,QA"
  }
  

• Yu Chen, Lingfei Wu, and Mohammed J. Zaki. Reinforcement learning based graph-to-sequence model for natural question generation. In NeurIPS 2019 Workshop on Graph Representation Learning. December 2019. URL: https://grlearning.github.io/papers/.
  [full text] [BibTeX▼]

  @inproceedings{2020-GRL,
      author = "Chen, Yu and Wu, Lingfei and Zaki, Mohammed J.",
      title = "Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation",
      booktitle = "NeurIPS 2019 Workshop on Graph Representation Learning",
      year = "2019",
      month = "December",
      url = "https://grlearning.github.io/papers/",
      KeyWord = "Conference,GraphMining,NeuralNetworks,QA"
  }
  

2018

• Mohamed Elshrif, Stefano G. Rizzo, Franz D. Betz, Dragos D. Margineantu, Mohammed J. Zaki, and Sanjay Chawla. Embeddings for the identification of aircraft faults (merit). In IEEE International Conference on Prognostics and Health Management. Jun 2018.
  [full text] [BibTeX▼]

  @inproceedings{2018-phm,
      author = "Elshrif, Mohamed and Rizzo, Stefano G. and Betz, Franz D. and Margineantu, Dragos D. and Zaki, Mohammed J. and Chawla, Sanjay",
      title = "Embeddings for the Identification of Aircraft Faults (MERIT)",
      booktitle = "IEEE International Conference on Prognostics and Health Management",
      month = "Jun",
      year = "2018",
      KeyWord = "Conference,NeuralNetworks"
  }
  

• Vipula Rawte, Aparna Gupta, and Mohammed J. Zaki. Analysis of year-over-year changes in risk factors disclosure in 10-k filings. In Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets, DSMM'18, 8:1–8:4. New York, NY, USA, 2018. ACM. doi:10.1145/3220547.3220555.
  [full text] [BibTeX▼]

  @inproceedings{2018-dsmm,
      author = "Rawte, Vipula and Gupta, Aparna and Zaki, Mohammed J.",
      title = "Analysis of Year-over-year Changes in Risk Factors Disclosure in 10-K Filings",
      booktitle = "Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets",
      series = "DSMM'18",
      year = "2018",
      isbn = "978-1-4503-5883-5",
      location = "Houston, TX, USA",
      pages = "8:1--8:4",
      articleno = "8",
      numpages = "4",
      doi = "10.1145/3220547.3220555",
      acmid = "3220555",
      publisher = "ACM",
      address = "New York, NY, USA",
      keywords = "Workshop,TextMining"
  }
  

• Vipula Rawte, Aparna Gupta, and Mohammed J. Zaki. Using supervised learning techniques for entity relationships. In Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets, DSMM'18, 13:1–13:2. New York, NY, USA, 2018. ACM. doi:10.1145/3220547.3226044.
  [full text] [BibTeX▼]

  @inproceedings{2018-dsmm-feiii,
      author = "Rawte, Vipula and Gupta, Aparna and Zaki, Mohammed J.",
      title = "Using Supervised Learning Techniques for Entity Relationships",
      booktitle = "Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets",
      series = "DSMM'18",
      year = "2018",
      isbn = "978-1-4503-5883-5",
      location = "Houston, TX, USA",
      pages = "13:1--13:2",
      articleno = "13",
      numpages = "2",
      doi = "10.1145/3220547.3226044",
      acmid = "3226044",
      publisher = "ACM",
      address = "New York, NY, USA",
      keywords = "Workshop,TextMining"
  }
  

• Xin Gao, Jake Y. Chen, and Mohammed J. Zaki. Multiscale and multimodal analysis for computational biology. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(6):1951–1952, 2018. doi:10.1109/TCBB.2018.2838658.
  [BibTeX▼]

  @article{2018-tcbb,
      author = "Gao, Xin and Chen, Jake Y. and Zaki, Mohammed J.",
      title = "Multiscale and Multimodal Analysis for Computational Biology",
      journal = "{IEEE/ACM} Transactions on Computational Biology and Bioinformatics",
      volume = "15",
      number = "6",
      pages = "1951--1952",
      year = "2018",
      doi = "10.1109/TCBB.2018.2838658",
      KeyWord = "Journal,GuestEditor,Bioinformatics"
  }
  

2017

• Eslam Hussain, Abdurrahman Ghanem, Vinicius Vitor dos Santos Dias, Carlos H.C. Teixeira, Ghadeer AbuOda, Marco Serafini, Georgos Siganos, Gianmarco De Francisci Morales, Ashraf Aboulnaga, and Mohammed J. Zaki. Graph data mining with arabesque. In Proceedings of the ACM SIGMOD International Conference on Management of Data (Demo Track). May 2017. doi:10.1145/3035918.3058742.
  [full text] [BibTeX▼]

  @inproceedings{2017-sigmod,
      author = "Hussain, Eslam and Ghanem, Abdurrahman and dos Santos Dias, Vinicius Vitor and Teixeira, Carlos H.C. and AbuOda, Ghadeer and Serafini, Marco and Siganos, Georgos and Morales, Gianmarco De Francisci and Aboulnaga, Ashraf and Zaki, Mohammed J.",
      title = "Graph Data Mining with Arabesque",
      booktitle = "Proceedings of the ACM SIGMOD International Conference on Management of Data (Demo Track)",
      month = "May",
      DOI = "10.1145/3035918.3058742",
      year = "2017",
      KeyWord = "Conference,GraphMining,DistributedMining,BigData"
  }
  

• Mohammed J. Zaki. Closed itemset mining and non-redundant association rule mining. In Ling Liu and M. Tamer Ozsu, editors, Encyclopedia of Database Systems. Springer-Verlag, 2017. doi:10.1007/978-1-4899-7993-3_66-2.
  [full text] [BibTeX▼]

  @incollection{2017-eds,
      Author = "Zaki, Mohammed J.",
      Editor = "Liu, Ling and Ozsu, M. Tamer",
      Title = "Closed Itemset Mining and Non-redundant Association Rule Mining",
      Booktitle = "Encyclopedia of Database Systems",
      Publisher = "Springer-Verlag",
      Year = "2017",
      DOI = "10.1007/978-1-4899-7993-3\_66-2",
      KeyWord = "Chapter,ItemsetMining"
  }
  

• Yu Chen and Mohammed J. Zaki. Kate: k-competitive autoencoder for text. In ACM SIGKDD International Conference on Data Mining and Knowledge Discovery. Aug 2017. doi:10.1145/3097983.3098017.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2017-sigkdd,
      author = "Chen, Yu and Zaki, Mohammed J.",
      title = "KATE: K-Competitive Autoencoder for Text",
      booktitle = "ACM SIGKDD International Conference on Data Mining and Knowledge Discovery",
      DOI = "10.1145/3097983.3098017",
      year = "2017",
      month = "Aug",
      customlinkgithubcode = "https://github.com/zakimjz/KATE",
      KeyWord = "Conference,TextMining,NeuralNetworks,AutoEncoders"
  }
  

• Yu Chen, Rhaad M. Rabbani, Aparna Gupta, and Mohammed J. Zaki. Comparative text analytics via topic modeling in banking. In IEEE Symposium on Computational Intelligence for Financial Engineering and Economics. 2017.
  [full text] [BibTeX▼]

  @inproceedings{2017-cifer,
      author = "Chen, Yu and Rabbani, Rhaad M. and Gupta, Aparna and Zaki, Mohammed J.",
      title = "Comparative Text Analytics via Topic Modeling in Banking",
      booktitle = "IEEE Symposium on Computational Intelligence for Financial Engineering and Economics",
      year = "2017",
      KeyWord = "Conference,TextMining"
  }
  

2016

• Geng Li and Mohammed J. Zaki. Sampling frequent and minimal boolean patterns: theory and application in classification. Data Mining and Knowledge Discovery, 30(1):181–225, January 2016. doi:10.1007/s10618-015-0409-y.
  [full text] [githubcode] [BibTeX▼]

  @article{2016-dmkd,
      Author = "Li, Geng and Zaki, Mohammed J.",
      Title = "Sampling Frequent and Minimal Boolean Patterns: Theory and Application in Classification",
      Journal = "Data Mining and Knowledge Discovery",
      Year = "2016",
      Volume = "30",
      number = "1",
      pages = "181--225",
      Month = "January",
      doi = "10.1007/s10618-015-0409-y",
      customlinkgithubcode = "https://github.com/zakimjz/minDNF",
      KeyWord = "Journal,DataMining,ItemsetMining,BooleanPatterns,Sampling"
  }
  

• Luam C. Totti, Prasenjit Mitra, Mourad Ouzzani, and Mohammed J. Zaki. A query-oriented approach for relevance in citation networks. In Proceedings of the 25th International Conference Companion on World Wide Web: 3rd WWW Workshop on Big Scholarly Data: Towards the Web of Scholars, 401–406. WWW, 2016. doi:10.1145/2872518.2890518.
  [full text] [data] [BibTeX▼]

  @inproceedings{2016-bigscholar,
      author = "Totti, Luam C. and Mitra, Prasenjit and Ouzzani, Mourad and Zaki, Mohammed J.",
      title = "A Query-oriented Approach for Relevance in Citation Networks",
      booktitle = "Proceedings of the 25th International Conference Companion on World Wide Web: 3rd WWW Workshop on Big Scholarly Data: Towards the Web of Scholars",
      year = "2016",
      publisher = "WWW",
      pages = "401--406",
      doi = "10.1145/2872518.2890518",
      customlinkdata = "https://github.com/zakimjz/IQRA",
      KeyWord = "Conference,DataMining,SocialNetworks,CitationNetworks,TextMining"
  }
  

• Mirka Saarela, Bulent Yener, Mohammed J. Zaki, and Tommi Karkkainen. Predicting math performance from raw large-scale educational assessments data: a machine learning approach. In ICML Workshop on Machine Learning for Digital Education and Assessment Systems. 2016.
  [full text] [BibTeX▼]

  @inproceedings{2016-mldeas,
      author = "Saarela, Mirka and Yener, Bulent and Zaki, Mohammed J. and Karkkainen, Tommi",
      title = "Predicting Math Performance from Raw Large-Scale Educational Assessments Data: A Machine Learning Approach",
      booktitle = "ICML Workshop on Machine Learning for Digital Education and Assessment Systems",
      year = "2016",
      KeyWord = "Conference,DataMining,EducationalDataMining"
  }
  

• Divyakant Agrawal, Sanjay Chawla, Ahmed Elmagarmid, Zoi Kaoudi, Mourad Ouzzani, Paolo Papotti, Jorge Quiane, Nan Tang, and Mohammed J. Zaki. Road to freedom in big data analytics. In Proceedings of the 19th International Conference on Extending Database Technology (EDBT). 2016.
  [full text] [BibTeX▼]

  @inproceedings{2016-edbt,
      author = "Agrawal, Divyakant and Chawla, Sanjay and Elmagarmid, Ahmed and Kaoudi, Zoi and Ouzzani, Mourad and Papotti, Paolo and Quiane, Jorge and Tang, Nan and Zaki, Mohammed J.",
      title = "Road to Freedom in Big Data Analytics",
      booktitle = "Proceedings of the 19th International Conference on Extending Database Technology (EDBT)",
      year = "2016",
      KeyWord = "Conference,BigData,DataMining"
  }
  

• Aparna Gupta, Majeed Simaan, and Mohammed J. Zaki. When positive sentiment is not so positive: textual analytics and bank failures. Available at SSRN: Social Science Research Network, 2773939, 2016. doi:10.2139/ssrn.2773939.
  [full text] [BibTeX▼]

  @article{2016-ssrn,
      author = "Gupta, Aparna and Simaan, Majeed and Zaki, Mohammed J.",
      title = "When Positive Sentiment is Not so Positive: Textual Analytics and Bank Failures",
      journal = "Available at SSRN: Social Science Research Network, 2773939",
      doi = "10.2139/ssrn.2773939",
      year = "2016",
      KeyWord = "Conference,TextMining"
  }
  

• Aparna Gupta, Majeed Simaan, and Mohammed J. Zaki. Investigating bank failures using text mining. In IEEE Symposium Series on Computational Intelligence. 2016. doi:10.1109/SSCI.2016.7850006.
  [full text] [BibTeX▼]

  @inproceedings{2016-ssci,
      author = "Gupta, Aparna and Simaan, Majeed and Zaki, Mohammed J.",
      title = "Investigating bank failures using text mining",
      booktitle = "IEEE Symposium Series on Computational Intelligence",
      doi = "10.1109/SSCI.2016.7850006",
      year = "2016",
      KeyWord = "Conference,TextMining"
  }
  

• Nilothpal Talukder and Mohammed J. Zaki. A distributed approach for graph mining in massive networks. Data Mining and Knowledge Discovery: Special Issue on ECML/PKDD 2016 Journal Track Papers, 30(5):1024–1052, 2016. doi:10.1007/s10618-016-0466-x.
  [full text] [githubcode] [data] [BibTeX▼]

  @article{2016-dmkd-distgraph,
      Author = "Talukder, Nilothpal and Zaki, Mohammed J.",
      Title = "A Distributed Approach for Graph Mining in Massive Networks",
      Journal = "Data Mining and Knowledge Discovery: Special Issue on ECML/PKDD 2016 Journal Track Papers",
      Year = "2016",
      Volume = "30",
      Number = "5",
      pages = "1024--1052",
      doi = "10.1007/s10618-016-0466-x",
      customlinkgithubcode = "https://github.com/zakimjz/DistGraph",
      customlinkdata = "https://zenodo.org/record/3731769",
      KeyWord = "Journal,GraphMining,BigData,DistributedMining,ParallelDM"
  }
  

• Divy Agrawal, Mouhamadou Lamine Ba, Laure Berti-Equille, Sanjay Chawla, Ahmed K. Elmagarmid, Hossam Hammady, Yasser Idris, Zoi Kaoudi, Zuhair Khayyat, Sebastian Kruse, Mourad Ouzzani, Paolo Papotti, Jorge-Arnulfo Quiane-Ruiz, Nan Tang, and Mohammed J. Zaki. Rheem: enabling multi-platform task execution. In Proceedings of the ACM SIGMOD International Conference on Management of Data (Demo Track), 2069–2072. 2016. doi:10.1145/2882903.2899414.
  [full text] [BibTeX▼]

  @inproceedings{2016-sigmod,
      author = "Agrawal, Divy and Ba, Mouhamadou Lamine and Berti-Equille, Laure and Chawla, Sanjay and Elmagarmid, Ahmed K. and Hammady, Hossam and Idris, Yasser and Kaoudi, Zoi and Khayyat, Zuhair and Kruse, Sebastian and Ouzzani, Mourad and Papotti, Paolo and Quiane-Ruiz, Jorge-Arnulfo and Tang, Nan and Zaki, Mohammed J.",
      title = "Rheem: Enabling Multi-Platform Task Execution",
      booktitle = "Proceedings of the ACM SIGMOD International Conference on Management of Data (Demo Track)",
      pages = "2069--2072",
      year = "2016",
      doi = "10.1145/2882903.2899414",
      KeyWord = "Conference,BigData"
  }
  

• Nilothpal Talukder and Mohammed J. Zaki. Parallel graph mining with dynamic load balancing. In 3rd International Workshop on High Performance Big Graph Data Management, Analysis, and Mining (with IEEE Big Data Conference). Dec 2016.
  [full text] [BibTeX▼]

  @inproceedings{2016-biggraphs,
      author = "Talukder, Nilothpal and Zaki, Mohammed J.",
      title = "Parallel Graph Mining with Dynamic Load Balancing",
      booktitle = "3rd International Workshop on High Performance Big Graph Data Management, Analysis, and Mining (with IEEE Big Data Conference)",
      month = "Dec",
      year = "2016",
      KeyWord = "Workshop,GraphMining,BigData,DistributedMining,ParallelDM"
  }
  

2015

• Gesse Dafe, Adriano Veloso, Mohammed J. Zaki, and Wagner Meira Jr. Learning sequential classifiers from long and noisy discrete-event sequences efficiently. Data Mining and Knowledge Discovery, 29(6):1685–1708, November 2015. doi:10.1007/s10618-014-0391-9.
  [full text] [BibTeX▼]

  @article{2015-dmkd-seq,
      Author = "Dafe, Gesse and Veloso, Adriano and Zaki, Mohammed J. and Jr, Wagner Meira",
      Title = "Learning sequential classifiers from long and noisy discrete-event sequences efficiently",
      Journal = "Data Mining and Knowledge Discovery",
      Year = "2015",
      Volume = "29",
      Number = "6",
      Month = "November",
      Pages = "1685-1708",
      doi = "10.1007/s10618-014-0391-9",
      KeyWord = "Journal,DataMining,SequenceMining,Classification"
  }
  

• Sarath Chandra Janga, Dongxiao Zhu, Jake Y. Chen, and Mohammed J. Zaki. Knowledge discovery using big data in biomedical systems. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(4):726–728, 2015. doi:10.1109/TCBB.2015.2454551.
  [BibTeX▼]

  @article{2015-tcbb,
      author = "Janga, Sarath Chandra and Zhu, Dongxiao and Chen, Jake Y. and Zaki, Mohammed J.",
      title = "Knowledge Discovery Using Big Data in Biomedical Systems",
      journal = "{IEEE/ACM} Transactions on Computational Biology and Bioinformatics",
      volume = "12",
      number = "4",
      pages = "726--728",
      year = "2015",
      doi = "10.1109/TCBB.2015.2454551",
      KeyWord = "Journal,Biokdd"
  }
  

• Carlos H. C. Teixeira, Alexandre J. Fonseca, Marco Serafini, Georgios Siganos, Mohammed J. Zaki, and Ashraf Aboulnaga. Arabesque: a system for distributed graph pattern mining. In Proceedings of the 25th ACM Symposium on Operating Systems Principles (SOSP). October 2015.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2015-sosp,
      Author = "Teixeira, Carlos H. C. and Fonseca, Alexandre J. and Serafini, Marco and Siganos, Georgios and Zaki, Mohammed J. and Aboulnaga, Ashraf",
      Title = "Arabesque: A System for Distributed Graph Pattern Mining",
      Booktitle = "Proceedings of the 25th ACM Symposium on Operating Systems Principles (SOSP)",
      Month = "October",
      Year = "2015",
      customlinkgithubcode = "https://github.com/zakimjz/Arabesque",
      KeyWord = "Conference,GraphMining,BigData,DistributedMining,ParallelDM"
  }
  

2014

• Rene Rodrigues Veloso, Loic Cerf, Wagner Meira Junior, and Mohammed J. Zaki. Reachability queries in very large graphs: a fast refined online search approach. In Proceedings of the 17th International Conference on Extending Database Technology (EDBT), 511–522. March 2014. doi:10.5441/002/edbt.2014.46.
  [full text] [BibTeX▼]

  @inproceedings{2014-edbt,
      author = "Veloso, Rene Rodrigues and Cerf, Loic and Junior, Wagner Meira and Zaki, Mohammed J.",
      title = "Reachability Queries in Very Large Graphs: A Fast Refined Online Search Approach",
      booktitle = "Proceedings of the 17th International Conference on Extending Database Technology (EDBT)",
      year = "2014",
      month = "March",
      pages = "511--522",
      doi = "10.5441/002/edbt.2014.46",
      KeyWord = "Conference,GraphIndexing,Indexing"
  }
  

• Mohammed J. Zaki, Zoran Obradovic, Pang-Ning Tan, Arindam Banerjee, Chandrika Kamath, and Srinivasan Parthasarathy, editors. Proceedings of the 2014 SIAM International Conference on Data Mining, Philadelphia, Pennsylvania, USA, April 24-26, 2014, SIAM, 2014. doi:10.1137/1.9781611973440.
  [BibTeX▼]

  @proceedings{2014-sdm,
      editor = "Zaki, Mohammed J. and Obradovic, Zoran and Tan, Pang-Ning and Banerjee, Arindam and Kamath, Chandrika and Parthasarathy, Srinivasan",
      title = "Proceedings of the 2014 {SIAM} International Conference on Data Mining, Philadelphia, Pennsylvania, USA, April 24-26, 2014",
      year = "2014",
      publisher = "SIAM",
      doi = "10.1137/1.9781611973440",
      isbn = "978-1-61197-344-0",
      KeyWord = "Book,DataMining"
  }
  

• Mohammed J. Zaki and Jr. Wagner Meira. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014. ISBN 9780521766333. URL: http://www.dataminingbook.info.
  [BibTeX▼]

  @book{2014-dmbook,
      author = "Zaki, Mohammed J. and Wagner Meira, Jr.",
      title = "Data Mining and Analysis: Fundamental Concepts and Algorithms",
      Publisher = "Cambridge University Press",
      year = "2014",
      isbn = "9780521766333",
      KeyWord = "Book,DataMining",
      url = "http://www.dataminingbook.info"
  }
  

• Robert Kessl, Nilothpal Talukder, Pranay Anchuri, and Mohammed J. Zaki. Parallel graph mining with GPUs. Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (with SIGKDD'14), Journal of Machine Learning Research: Conference and Workshop Proceedings, 36:1–36, 2014. URL: http://jmlr.org/proceedings/papers/v36/.
  [full text] [githubcode] [BibTeX▼]

  @article{2014-bigmine,
      Author = "Kessl, Robert and Talukder, Nilothpal and Anchuri, Pranay and Zaki, Mohammed J.",
      Title = "Parallel Graph Mining with {GPUs}",
      Journal = "Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (with SIGKDD'14), Journal of Machine Learning Research: Conference and Workshop Proceedings",
      Volume = "36",
      Pages = "1-36",
      Year = "2014",
      url = "http://jmlr.org/proceedings/papers/v36/",
      customlinkgithubcode = "https://github.com/zakimjz/GPU\_graph\_mining",
      KeyWord = "Conference,GraphMining,ParallelDM"
  }
  

• Benjarath Pupacdi, Asif Javed, Mohammed J. Zaki, and Mathuros Ruchirawat. NSIT: novel sequence identification tool. PLoS ONE, 9(9):e108011, 2014. doi:10.1371/journal.pone.0108011.
  [full text] [BibTeX▼]

  @article{2014-plos,
      Author = "Pupacdi, Benjarath and Javed, Asif and Zaki, Mohammed J. and Ruchirawat, Mathuros",
      Title = "{NSIT}: Novel Sequence Identification Tool",
      Journal = "PLoS ONE",
      Volume = "9",
      Number = "9",
      Pages = "e108011",
      Year = "2014",
      Pmid = "25264906",
      doi = "10.1371/journal.pone.0108011",
      KeyWord = "Journal,Bioseq,Indexing"
  }
  

2013

• Gaurav Pandey, Huzefa Rangwala, George Karypis, Jake Yue Chen, and Mohammed J. Zaki, editors. Proceedings of the 12th International Workshop on Data Mining in Bioinformatics, BIOKDD 2013, Chicago, IL, USA, August 11, 2013, ACM, 2013. URL: http://dl.acm.org/citation.cfm?id=2500863.
  [BibTeX▼]

  @proceedings{2013-biokdd,
      editor = "Pandey, Gaurav and Rangwala, Huzefa and Karypis, George and Chen, Jake Yue and Zaki, Mohammed J.",
      title = "Proceedings of the 12th International Workshop on Data Mining in Bioinformatics, BIOKDD 2013, Chicago, IL, USA, August 11, 2013",
      year = "2013",
      publisher = "{ACM}",
      url = "http://dl.acm.org/citation.cfm?id=2500863",
      isbn = "978-1-4503-2327-7",
      KeyWord = "Book,Biokdd"
  }
  

• Hilmi Yildirim, Vineet Chaoji, and Mohammed J. Zaki. Dagger: a scalable index for reachability queries in large dynamic graphs. arXiv Computing Research Repository, 2013. URL: http://arxiv.org/abs/1301.0977.
  [full text] [githubcode] [data] [BibTeX▼]

  @article{2013-dagger,
      author = "Yildirim, Hilmi and Chaoji, Vineet and Zaki, Mohammed J.",
      title = "DAGGER: A Scalable Index for Reachability Queries in Large Dynamic Graphs",
      journal = "arXiv Computing Research Repository",
      volume = "abs/1301.0977",
      year = "2013",
      url = "http://arxiv.org/abs/1301.0977",
      customlinkgithubcode = "https://github.com/zakimjz/dagger-index",
      customlinkdata = "https://github.com/zakimjz/dagger-index/tree/master/Downloads",
      keyword = "GraphIndexing,TechReport"
  }
  

• Geng Li, Stephan Gunnemann, and Mohammed J. Zaki. Stochastic subspace search for top-k multi-view clustering. In 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering (with SIGKDD'13). August 2013.
  [full text] [BibTeX▼]

  @inproceedings{2013-multiclust,
      author = "Li, Geng and Gunnemann, Stephan and Zaki, Mohammed J.",
      title = "Stochastic Subspace Search for Top-K Multi-View Clustering",
      booktitle = "4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering (with SIGKDD'13)",
      year = "2013",
      month = "August",
      keyword = "Conference,Clustering"
  }
  

• Arlei Silva, Sara Guimaraes, Wagner Meira Jr., and Mohammed J. Zaki. Profilerank: finding relevant content and influential users based on information diffusion. In The 7th SNAKDD Workshop on Social Network Mining and Analysis (with SIGKDD'13). August 2013.
  [full text] [BibTeX▼]

  @inproceedings{2013-snakdd,
      author = "Silva, Arlei and Guimaraes, Sara and Jr., Wagner Meira and Zaki, Mohammed J.",
      title = "ProfileRank: Finding Relevant Content and Influential Users based on Information Diffusion",
      booktitle = "The 7th SNAKDD Workshop on Social Network Mining and Analysis (with SIGKDD'13)",
      year = "2013",
      month = "August",
      keyword = "Conference,SocialNetworks"
  }
  

• Pranay Anchuri, Mohammed J. Zaki, Omer Barkol, Shahar Golan, and Moshe Shamy. Approximate graph mining with label costs. In 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2013. URL: http://dl.acm.org/citation.cfm?doid=2487575.2487602.
  [full text] [BibTeX▼]

  @inproceedings{2013-sigkdd,
      author = "Anchuri, Pranay and Zaki, Mohammed J. and Barkol, Omer and Golan, Shahar and Shamy, Moshe",
      title = "Approximate Graph Mining with Label Costs",
      Booktitle = "19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      Month = "August",
      Year = "2013",
      url = "http://dl.acm.org/citation.cfm?doid=2487575.2487602",
      KeyWord = "Conference,GraphMining"
  }
  

• Chandan K. Reddy, Mohammad Al Hasan, and Mohammed J. Zaki. Clustering biological data. In Charu C. Aggarwal and Chandan K. Reddy, editors, Data Clustering: Algorithms and Applications, pages 381–414. CRC Press, 2013.
  [BibTeX▼]

  @incollection{2013-crc,
      author = "Reddy, Chandan K. and Hasan, Mohammad Al and Zaki, Mohammed J.",
      editor = "Aggarwal, Charu C. and Reddy, Chandan K.",
      title = "Clustering Biological Data",
      booktitle = "Data Clustering: Algorithms and Applications",
      year = "2013",
      pages = "381--414",
      publisher = "CRC Press",
      KeyWord = "Chapter,Bioinformatics,Biokdd,DataMining"
  }
  

• Apirak Hoonlor, Boleslaw K. Szymanski, and Mohammed J. Zaki. Trends in computer science research. Communications of the ACM, 56(10):74–83, October 2013. doi:10.1145/2500892.
  [full text] [BibTeX▼]

  @article{2013-cacm,
      author = "Hoonlor, Apirak and Szymanski, Boleslaw K. and Zaki, Mohammed J.",
      title = "Trends in Computer science Research",
      journal = "Communications of the ACM",
      volume = "56",
      number = "10",
      month = "October",
      year = "2013",
      pages = "74--83",
      Doi = "10.1145/2500892",
      keyword = "Journal,SocialNetworks,DataMining,TextMining"
  }
  

2012

• Tamer Kahveci, Saeed Salem, Mehmet Koyuturk, Jake Yue Chen, and Mohammed J. Zaki, editors. Proceedings of the 11th International Workshop on Data Mining in Bioinformatics, BIOKDD 2012, Beijing, China, August 12, 2012, ACM, 2012. URL: http://dl.acm.org/citation.cfm?id=2350176.
  [BibTeX▼]

  @proceedings{2012-biokdd,
      editor = "Kahveci, Tamer and Salem, Saeed and Koyuturk, Mehmet and Chen, Jake Yue and Zaki, Mohammed J.",
      title = "Proceedings of the 11th International Workshop on Data Mining in Bioinformatics, {BIOKDD} 2012, Beijing, China, August 12, 2012",
      year = "2012",
      publisher = "{ACM}",
      url = "http://dl.acm.org/citation.cfm?id=2350176",
      isbn = "978-1-4503-1552-4",
      KeyWord = "Book,Biokdd,DataMining,Bioinformatics"
  }
  

• Vibin Ramakrishnan, Sai Praveen Srinivasan, Saeed Salem, Suzanne Matthews, Wilfredo Colon, Mohammed J. Zaki, and Chris Bystroff. GeoFold: topology-based protein unfolding pathways capture the effects of engineered disulfides on kinetic stability. Proteins: Structure, Function, and Bioinformatics, 80(3):920–934, March 2012. doi:10.1002/prot.23249/full.
  [full text] [BibTeX▼]

  @article{2012-proteins,
      Author = "Ramakrishnan, Vibin and Srinivasan, Sai Praveen and Salem, Saeed and Matthews, Suzanne and Colon, Wilfredo and Zaki, Mohammed J. and Bystroff, Chris",
      Title = "{GeoFold}: Topology-based protein unfolding pathways capture the effects of engineered disulfides on kinetic stability",
      Journal = "Proteins: Structure, Function, and Bioinformatics",
      Volume = "80",
      Number = "3",
      Pages = "920-934",
      Year = "2012",
      Month = "March",
      doi = "10.1002/prot.23249/full",
      KeyWord = "Journal,Proteins"
  }
  

• Arlei Silva, Mohammed J. Zaki, and Wagner Meira Jr. Mining attribute-structure correlated patterns in large attributed graphs. PVLDB, 5(5):466–477, 2012.
  [full text] [githubcode] [data] [BibTeX▼]

  @article{2012-vldb,
      author = "Silva, Arlei and Zaki, Mohammed J. and Jr., Wagner Meira",
      title = "Mining Attribute-structure Correlated Patterns in Large Attributed Graphs",
      journal = "PVLDB",
      volume = "5",
      number = "5",
      year = "2012",
      pages = "466--477",
      customlinkgithubcode = "https://github.com/zakimjz/SCPM",
      customlinkdata = "https://zenodo.org/record/3732153",
      KeyWord = "Journal,GraphMining,Clustering"
  }
  

• Glivia A.R. Barbosa, Wagner Meira, Ismail A. Silva, Raquel O. Prates, Mohammed J. Zaki, and Adriano Veloso. Characterizing the effectiveness of twitter hashtags to detect and track online population sentiment. In ACM SIGCHI Conference on Human Factors in Computing Systems – Juried Works-in-Progress. May 2012.
  [full text] [BibTeX▼]

  @inproceedings{2012-chi,
      author = "Barbosa, Glivia A.R. and Meira, Wagner and Silva, Ismail A. and Prates, Raquel O. and Zaki, Mohammed J. and Veloso, Adriano",
      title = "Characterizing the Effectiveness of Twitter Hashtags to Detect and Track Online Population Sentiment",
      Booktitle = "ACM SIGCHI Conference on Human Factors in Computing Systems -- Juried Works-in-Progress",
      month = "May",
      year = "2012",
      KeyWord = "Conference,SocialNetworks,SentimentAnalysis,DataMining,Classification"
  }
  

• Medha Atre, Vineet Chaoji, and Mohammed J. Zaki. Bitpath – label order constrained reachability queries over large graphs. arXiv Computing Research Repository, 2012. URL: http://arxiv.org/abs/1203.2886.
  [full text] [BibTeX▼]

  @article{2012-arxiv,
      Author = "Atre, Medha and Chaoji, Vineet and Zaki, Mohammed J.",
      Title = "BitPath -- Label Order Constrained Reachability Queries over Large Graphs",
      Year = "2012",
      journal = "arXiv Computing Research Repository",
      volume = "abs:1203.2886",
      url = "http://arxiv.org/abs/1203.2886",
      KeyWord = "TechReport,GraphIndexing,Indexing"
  }
  

• Mohammad Al Hasan, Jun Huan, Jake Y. Chen, and Mohammed J. Zaki. Biological knowledge discovery and data mining. Scientific Programming, 20(1):1–2, 2012.
  [full text] [BibTeX▼]

  @article{2012-sp:editorial,
      Author = "Hasan, Mohammad Al and Huan, Jun and Chen, Jake Y. and Zaki, Mohammed J.",
      Title = "Biological Knowledge Discovery and Data Mining",
      Journal = "Scientific Programming",
      Year = "2012",
      Volume = "20",
      Number = "1",
      Pages = "1-2",
      KeyWord = "Journal,Biokdd"
  }
  

• Geng Li, Murat Semerci, Bulent Yener, and Mohammed J. Zaki. Effective graph classification based on topological and label attributes. Statistical Analysis and Data Mining, 5(4):265–283, August 2012. doi:10.1002/sam.11153.
  [full text] [BibTeX▼]

  @article{2012-sadm,
      Author = "Li, Geng and Semerci, Murat and Yener, Bulent and Zaki, Mohammed J.",
      Title = "Effective Graph Classification based on Topological and Label Attributes",
      Journal = "Statistical Analysis and Data Mining",
      Volume = "5",
      Number = "4",
      Pages = "265-283",
      Month = "August",
      Year = "2012",
      doi = "10.1002/sam.11153",
      KeyWord = "Journal,GraphMining,Classification"
  }
  

• Hilmi Yildirim, Vineet Chaoji, and Mohammed J. Zaki. Grail: a scalable index for reachability queryies in very large graphs. The VLDB Journal, 21(4):509–534, August 2012. doi:10.1007/s00778-011-0256-4.
  [full text] [githubcode] [data] [BibTeX▼]

  @article{2012-grail-jvldb,
      Author = "Yildirim, Hilmi and Chaoji, Vineet and Zaki, Mohammed J.",
      Title = "GRAIL: A Scalable Index for Reachability Queryies in Very Large Graphs",
      Journal = "The VLDB Journal",
      Volume = "21",
      Number = "4",
      Month = "August",
      doi = "10.1007/s00778-011-0256-4",
      Pages = "509-534",
      Year = "2012",
      customlinkgithubcode = "https://github.com/zakimjz/grail",
      customlinkdata = "https://github.com/zakimjz/grail/tree/master/Downloads",
      KeyWord = "Journal,GraphIndexing,Indexing"
  }
  

• Geng Li and Mohammed J. Zaki. Sampling minimal frequent boolean (dnf) patterns. In 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2012.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2012-sigkdd,
      Author = "Li, Geng and Zaki, Mohammed J.",
      Title = "Sampling Minimal Frequent Boolean (DNF) Patterns",
      Booktitle = "18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      Month = "August",
      Year = "2012",
      customlinkgithubcode = "https://github.com/zakimjz/minDNF",
      KeyWord = "Conference,ItemsetMining,BooleanPatterns"
  }
  

• Pranay Anchuri, Mohammed J. Zaki, Omer Barkol, Ruth Bergman, Yifat Felder, Shahar Golan, and Arik Sityon. Graph mining for discovering infrastructure patterns in configuration management databases. Knowledge and Information Systems, 33(3):491–522, December 2012. doi:10.1007/s10115-012-0528-3.
  [full text] [BibTeX▼]

  @article{2012-kais,
      Author = "Anchuri, Pranay and Zaki, Mohammed J. and Barkol, Omer and Bergman, Ruth and Felder, Yifat and Golan, Shahar and Sityon, Arik",
      Title = "Graph Mining for Discovering Infrastructure Patterns in Configuration Management Databases",
      journal = "Knowledge and Information Systems",
      volume = "33",
      number = "3",
      year = "2012",
      pages = "491-522",
      month = "December",
      doi = "10.1007/s10115-012-0528-3",
      KeyWord = "Journal,GraphMining"
  }
  

• Helio Almeida, Dorgival Olavo Guedes Neto, Wagner Meira Jr., and Mohammed J. Zaki. Towards a better quality metric for graph cluster evaluation. Journal of Information and Data Management, 3(3):378–393, October 2012.
  [full text] [BibTeX▼]

  @article{2012-jidm,
      Author = "Almeida, Helio and Neto, Dorgival Olavo Guedes and Jr., Wagner Meira and Zaki, Mohammed J.",
      Title = "Towards a better quality metric for graph cluster evaluation",
      journal = "Journal of Information and Data Management",
      Month = "October",
      volume = "3",
      number = "3",
      pages = "378-393",
      Year = "2012",
      KeyWord = "Journal,GraphMining,Clustering"
  }
  

• Apirak Hoonlor, Boleslaw K. Szymanski, Mohammed J. Zaki, and Vineet Chaoji. Document clustering with bursty information. Computing and Informatics, 31(6+):1533–1555, 2012.
  [full text] [BibTeX▼]

  @article{2012-cai,
      Author = "Hoonlor, Apirak and Szymanski, Boleslaw K. and Zaki, Mohammed J. and Chaoji, Vineet",
      Title = "Document clustering with bursty information",
      Journal = "Computing and Informatics",
      volume = "31",
      number = "6+",
      pages = "1533-1555",
      Year = "2012",
      KeyWord = "Journal,Clustering"
  }
  

• Xue-wen Chen, Guy Lebanon, Haixun Wang, and Mohammed J. Zaki, editors. 21st ACM International Conference on Information and Knowledge Management, CIKM'12, Maui, HI, USA, October 29 - November 02, 2012, ACM, 2012. URL: http://dl.acm.org/citation.cfm?id=2396761.
  [BibTeX▼]

  @proceedings{2012-cikm,
      editor = "Chen, Xue-wen and Lebanon, Guy and Wang, Haixun and Zaki, Mohammed J.",
      title = "21st ACM International Conference on Information and Knowledge Management, CIKM'12, Maui, HI, USA, October 29 - November 02, 2012",
      booktitle = "CIKM",
      publisher = "ACM",
      year = "2012",
      isbn = "978-1-4503-1156-4",
      url = "http://dl.acm.org/citation.cfm?id=2396761",
      KeyWord = "Book,DataMining"
  }
  

• Mohammed J. Zaki, Arno Siebes, Jeffrey Xu Yu, Bart Goethals, Geoffrey I. Webb, and Xindong Wu, editors. 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, December 10-13, 2012, IEEE Computer Society, 2012. URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6412852.
  [BibTeX▼]

  @proceedings{2012-icdm,
      editor = "Zaki, Mohammed J. and Siebes, Arno and Yu, Jeffrey Xu and Goethals, Bart and Webb, Geoffrey I. and Wu, Xindong",
      title = "12th {IEEE} International Conference on Data Mining, {ICDM} 2012, Brussels, Belgium, December 10-13, 2012",
      year = "2012",
      publisher = "{IEEE} Computer Society",
      url = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6412852",
      isbn = "978-1-4673-4649-8",
      KeyWord = "Book,DataMining"
  }
  

• Jilles Vreeken, Charles Ling, Mohammed J. Zaki, Arno Siebes, Jeffrey Xu Yu, Bart Goethals, Geoffrey I. Webb, and Xindong Wu, editors. 12th IEEE International Conference on Data Mining Workshops, ICDM Workshops, Brussels, Belgium, December 10, 2012, IEEE Computer Society, 2012. URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6403636.
  [BibTeX▼]

  @proceedings{2012-icdmw,
      editor = "Vreeken, Jilles and Ling, Charles and Zaki, Mohammed J. and Siebes, Arno and Yu, Jeffrey Xu and Goethals, Bart and Webb, Geoffrey I. and Wu, Xindong",
      title = "12th {IEEE} International Conference on Data Mining Workshops, {ICDM} Workshops, Brussels, Belgium, December 10, 2012",
      year = "2012",
      publisher = "{IEEE} Computer Society",
      url = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6403636",
      isbn = "978-1-4673-5164-5",
      KeyWord = "Book,DataMining"
  }
  

2011

• Vineet Chaoji, Geng Li, Hilmi Yildirim, and Mohammed J. Zaki. ABACUS: mining arbitrary shaped clusters from large datasets based on backbone identification. In 11th SIAM International Conference on Data Mining. April 2011.
  [full text] [BibTeX▼]

  @inproceedings{2011-abacus,
      Author = "Chaoji, Vineet and Li, Geng and Yildirim, Hilmi and Zaki, Mohammed J.",
      Title = "{ABACUS}: Mining Arbitrary Shaped Clusters from Large Datasets based on Backbone Identification",
      Booktitle = "11th SIAM International Conference on Data Mining",
      Month = "April",
      Year = "2011",
      KeyWord = "Conference,Clustering"
  }
  

• Elisa B. de Lima, Raquel C. Minardi, Jr. Wagner Meira, and Mohammed J. Zaki. Data integration via constrained clustering: an application to enzyme clustering. In 11th SIAM International Conference on Data Mining. April 2011.
  [full text] [BibTeX▼]

  @inproceedings{2011-enzyme,
      Author = "de Lima, Elisa B. and Minardi, Raquel C. and Wagner Meira, Jr. and Zaki, Mohammed J.",
      Title = "Data Integration via Constrained Clustering: An Application to Enzyme Clustering",
      Booktitle = "11th SIAM International Conference on Data Mining",
      Month = "April",
      Year = "2011",
      KeyWord = "Conference,Clustering,Biokdd"
  }
  

• Adriano Veloso, Jr. Wagner Meira, Macros Goncalves, Humberto Almeida, and Mohammed J. Zaki. Calibrated lazy associative classification. Information Sciences, 181(13):2656–2670, July 2011.
  [full text] [BibTeX▼]

  @article{2011-lac-calibrated,
      Author = "Veloso, Adriano and Wagner Meira, Jr. and Goncalves, Macros and Almeida, Humberto and Zaki, Mohammed J.",
      Title = "Calibrated Lazy Associative Classification",
      journal = "Information Sciences",
      Month = "July",
      Year = "2011",
      Volume = "181",
      Number = "13",
      Pages = "2656-2670",
      KeyWord = "Journal,Classification"
  }
  

• Mohammad Al Hasan and Mohammed J. Zaki. A survey of link prediction in social networks. In Charu Aggarwal, editor, Social Network Data Analytics, chapter 9, pages 243–275. Springer, 2011.
  [full text] [BibTeX▼]

  @incollection{2011-linkprediction,
      Author = "Hasan, Mohammad Al and Zaki, Mohammed J.",
      Editor = "Aggarwal, Charu",
      Title = "A Survey of Link Prediction in Social Networks",
      Booktitle = "Social Network Data Analytics",
      Chapter = "9",
      Pages = "243-275",
      Publisher = "Springer",
      Year = "2011",
      KeyWord = "Chapter,Classification,SocialNetworks,GraphMining"
  }
  

• Geng Li, Murat Semerci, Bulent Yener, and Mohammed J. Zaki. Graph classification via topological and label attributes. In 9th Workshop on Mining and Learning with Graphs (with SIGKDD). August 2011.
  [full text] [BibTeX▼]

  @inproceedings{2011-mlg,
      Author = "Li, Geng and Semerci, Murat and Yener, Bulent and Zaki, Mohammed J.",
      Title = "Graph Classification via Topological and Label Attributes",
      Booktitle = "9th Workshop on Mining and Learning with Graphs (with SIGKDD)",
      Month = "August",
      Year = "2011",
      KeyWord = "Conference,GraphMining,Classification"
  }
  

• Helio Almeida, Dorgival Olavo Guedes Neto, Wagner Meira Jr., and Mohammed J. Zaki. Is there a best quality metric for graph clusters? In 15th European Conference on Principles and Practice of Knowledge Discovery in Databases. September 2011.
  [full text] [BibTeX▼]

  @inproceedings{2011-pkdd,
      Author = "Almeida, Helio and Neto, Dorgival Olavo Guedes and Jr., Wagner Meira and Zaki, Mohammed J.",
      Title = "Is there a best quality metric for graph clusters?",
      Booktitle = "15th European Conference on Principles and Practice of Knowledge Discovery in Databases",
      Month = "September",
      Year = "2011",
      KeyWord = "Conference,GraphMining,Clustering"
  }
  

• Pranay Anchuri, Mohammed J. Zaki, Omer Barkol, Ruth Bergman, Yifat Felder, Shahar Golan, and Arik Sityon. Infrastructure pattern discovery in configuration management databases via large sparse graph mining. In 11th IEEE International Conference on Data Mining. December 2011. *Best Papers Selection*.
  [full text] [BibTeX▼]

  @inproceedings{2011-icdm,
      Author = "Anchuri, Pranay and Zaki, Mohammed J. and Barkol, Omer and Bergman, Ruth and Felder, Yifat and Golan, Shahar and Sityon, Arik",
      Title = "Infrastructure Pattern Discovery in Configuration Management Databases via Large Sparse Graph Mining",
      Booktitle = "11th IEEE International Conference on Data Mining",
      Month = "December",
      Year = "2011",
      Note = "*Best Papers Selection*",
      KeyWord = "Conference,GraphMining"
  }
  

• Mohammad Al Hasan, Saeed Salem, and Mohammed J. Zaki. Simclus: an effective algorithm for clustering with a lower bound on similarity. Knowledge and Information Systems, 28(3):665–685, 2011.
  [full text] [BibTeX▼]

  @article{2011-simclus-kais,
      author = "Hasan, Mohammad Al and Salem, Saeed and Zaki, Mohammed J.",
      title = "SimClus: an effective algorithm for clustering with a lower bound on similarity",
      journal = "Knowledge and Information Systems",
      volume = "28",
      number = "3",
      year = "2011",
      pages = "665-685",
      KeyWord = "Journal,Clustering,GraphMining"
  }
  

• Mohammad Al Hasan, Jun Huan, Jake Y. Chen, and Mohammed J. Zaki, editors. Proceedings of BIOKDD11: 10th ACM SIGKDD International Workshop on Data Mining in Bioinformatics, 2011. URL: http://www.cs.rpi.edu/~zaki/Workshops/BIOKDD11/.
  [full text] [BibTeX▼]

  @proceedings{2011-biokdd,
      Author = "Hasan, Mohammad Al and Huan, Jun and Chen, Jake Y. and Zaki, Mohammed J.",
      Editor = "Hasan, Mohammad Al and Huan, Jun and Chen, Jake Y. and Zaki, Mohammed J.",
      Title = "Proceedings of BIOKDD11: 10th ACM SIGKDD International Workshop on Data Mining in Bioinformatics",
      Year = "2011",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/BIOKDD11/",
      KeyWord = "Book,Biokdd"
  }
  

• Fang-Xiang Wu, Mohammed J. Zaki, Shinichi Morishita, Yi Pan, Stephen Wong, Anastasia Christianson, and Xiaohua Hu, editors. IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011, Atlanta, GA, USA, 12-15 November, 2011, IEEE, 2011. URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6120121.
  [BibTeX▼]

  @proceedings{2011-bibm,
      editor = "Wu, Fang-Xiang and Zaki, Mohammed J. and Morishita, Shinichi and Pan, Yi and Wong, Stephen and Christianson, Anastasia and Hu, Xiaohua",
      title = "IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2011, Atlanta, GA, USA, 12-15 November, 2011",
      publisher = "IEEE",
      year = "2011",
      isbn = "978-1-4577-1799-4",
      url = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6120121",
      KeyWord = "Book,DataMining,Bioinformatics"
  }
  

2010

• Mohammed J. Zaki, Christopher D. Carothers, and Boleslaw K. Szymanski. VOGUE: a variable order hidden markov model with duration based on frequent sequence mining. ACM Transactions on Knowledge Discovery in Data, 4(1):Article 5, January 2010.
  [full text] [githubcode] [BibTeX▼]

  @article{2010-vogue:tkdd,
      Author = "Zaki, Mohammed J. and Carothers, Christopher D. and Szymanski, Boleslaw K.",
      Title = "{VOGUE}: A Variable Order Hidden Markov Model with Duration based on Frequent Sequence Mining",
      Journal = "ACM Transactions on Knowledge Discovery in Data",
      Volume = "4",
      Number = "1",
      Pages = "Article 5",
      Month = "January",
      Year = "2010",
      customlinkgithubcode = "https://github.com/zakimjz/VOGUE",
      KeyWord = "Journal,SequenceMining,Bioseq,HMM"
  }
  

• Saeed Salem, Mohammed J. Zaki, and Chris Bystroff. FlexSnap: flexible non-sequential protein structure alignment. Algorithms in Molecular Biology, January 2010. Special issue on best papers from WABI'09. URL: http://www.almob.org/content/5/1/12.
  [full text] [BibTeX▼]

  @article{2010-AMB,
      Author = "Salem, Saeed and Zaki, Mohammed J. and Bystroff, Chris",
      Title = "{FlexSnap}: Flexible Non-Sequential Protein Structure Alignment",
      Journal = "Algorithms in Molecular Biology",
      Note = "Special issue on best papers from WABI'09",
      Volume = "5",
      Number = "12",
      Month = "January",
      Year = "2010",
      Pmid = "20047669",
      url = "http://www.almob.org/content/5/1/12",
      KeyWord = "Journal,Proteins"
  }
  

• Karam Gouda, Mosab Hassaan, and Mohammed J. Zaki. PRISM: an effective approach for frequent sequence mining via prime-block encoding. Journal of Computer and Systems Sciences, 76(1):88–102, February 2010. Special issue on Intelligent Data Analysis. doi:10.1016/j.jcss.2009.05.008.
  [full text] [githubcode] [BibTeX▼]

  @article{2010-prism:jcss,
      Author = "Gouda, Karam and Hassaan, Mosab and Zaki, Mohammed J.",
      Title = "{PRISM}: An Effective Approach for Frequent Sequence Mining via Prime-Block Encoding",
      Journal = "Journal of Computer and Systems Sciences",
      Note = "Special issue on Intelligent Data Analysis",
      Volume = "76",
      Number = "1",
      Month = "February",
      Year = "2010",
      Pages = "88-102",
      Doi = "10.1016/j.jcss.2009.05.008",
      customlinkgithubcode = "https://github.com/zakimjz/PRISM",
      KeyWord = "Journal,SequenceMining"
  }
  

• Medha Atre, Vineet Chaoji, Mohammed J. Zaki, and James A. Hendler. Matrix bit loaded: a scalable lightweight join query processor for rdf data. In 19th International World Wide Web Conference. April 2010.
  [full text] [BibTeX▼]

  @inproceedings{2010-WWW,
      Author = "Atre, Medha and Chaoji, Vineet and Zaki, Mohammed J. and Hendler, James A.",
      Title = "Matrix Bit loaded: A Scalable Lightweight Join Query Processor for RDF Data",
      Booktitle = "19th International World Wide Web Conference",
      Month = "April",
      Year = "2010",
      KeyWord = "Conference,Indexing,RDF,Semantic"
  }
  

• Jierui Xie, Boleslaw Szymanski, and Mohammed J. Zaki. Learning dissimilarities for categorical symbols. In Workshop on Feature Selection in Data Mining (with PAKDD). June 2010. URL: http://jmlr.csail.mit.edu/proceedings/papers/v10/.
  [full text] [BibTeX▼]

  @inproceedings{2010-FSDM,
      Author = "Xie, Jierui and Szymanski, Boleslaw and Zaki, Mohammed J.",
      Title = "Learning Dissimilarities for Categorical Symbols",
      Booktitle = "Workshop on Feature Selection in Data Mining (with PAKDD)",
      Month = "June",
      Year = "2010",
      URL = "http://jmlr.csail.mit.edu/proceedings/papers/v10/",
      KeyWord = "Conference,Classification"
  }
  

• Mohammed J. Zaki, Jeffrey X. Yu, B. Ravindran, and Vikram Pudi. Advances in Knowledge Discovery and Data Mining, Part I, Proceedings of the 14th Pacific-Asia Conference (PAKDD 2010). Volume 6118 of LNAI. Springer, 2010. ISBN: 978-3-642-13656-6. URL: http://www.springerlink.com/content/978-3-642-13656-6/.
  [BibTeX▼]

  @book{2010-pakdd1,
      Author = "Zaki, Mohammed J. and Yu, Jeffrey X. and Ravindran, B. and Pudi, Vikram",
      Editor = "Zaki, Mohammed J. and Yu, Jeffrey X. and Ravindran, B. and Pudi, Vikram",
      Booktitle = "Advances in Knowledge Discovery and Data Mining, Part I, Proceedings of the 14th Pacific-Asia Conference (PAKDD 2010)",
      Title = "Advances in Knowledge Discovery and Data Mining, Part I, Proceedings of the 14th Pacific-Asia Conference (PAKDD 2010)",
      Publisher = "Springer",
      Series = "LNAI",
      Volume = "6118",
      Year = "2010",
      Note = "ISBN: 978-3-642-13656-6",
      Url = "http://www.springerlink.com/content/978-3-642-13656-6/",
      KeyWord = "Book,DataMining"
  }
  

• Mohammed J. Zaki, Jeffrey X. Yu, B. Ravindran, and Vikram Pudi. Advances in Knowledge Discovery and Data Mining, Part II, Proceedings of the 14th Pacific-Asia Conference (PAKDD 2010). Volume 6119 of LNAI. Springer, 2010. ISBN: 978-3-642-13671-9. URL: http://www.springerlink.com/content/978-3-642-13671-9/.
  [BibTeX▼]

  @book{2010-pakdd2,
      Author = "Zaki, Mohammed J. and Yu, Jeffrey X. and Ravindran, B. and Pudi, Vikram",
      Editor = "Zaki, Mohammed J. and Yu, Jeffrey X. and Ravindran, B. and Pudi, Vikram",
      Booktitle = "Advances in Knowledge Discovery and Data Mining, Part II, Proceedings of the 14th Pacific-Asia Conference (PAKDD 2010)",
      Title = "Advances in Knowledge Discovery and Data Mining, Part II, Proceedings of the 14th Pacific-Asia Conference (PAKDD 2010)",
      Publisher = "Springer",
      Series = "LNAI",
      Volume = "6119",
      Year = "2010",
      Note = "ISBN: 978-3-642-13671-9",
      Url = "http://www.springerlink.com/content/978-3-642-13671-9/",
      KeyWord = "Book,DataMining"
  }
  

• Mohammed J. Zaki. Practical graph mining. In 18th International Conference on Conceptual Structures, 13. 2010. doi:10.1007/978-3-642-14197-3_5.
  [BibTeX▼]

  @inproceedings{2010-iccs,
      author = "Zaki, Mohammed J.",
      title = "Practical Graph Mining",
      booktitle = "18th International Conference on Conceptual Structures",
      year = "2010",
      pages = "13",
      doi = "10.1007/978-3-642-14197-3\_5",
      KeyWord = "Conference,GraphMining"
  }
  

• Arlei Silva, Jr. Wagner Meira, and Mohammed J. Zaki. Structural correlation pattern mining for large graphs. In 8th Workshop on Mining and Learning with Graphs (with SIGKDD). July 2010.
  [full text] [githubcode] [data] [BibTeX▼]

  @inproceedings{2010-MLG,
      Author = "Silva, Arlei and Wagner Meira, Jr. and Zaki, Mohammed J.",
      Title = "Structural Correlation Pattern Mining for Large Graphs",
      Booktitle = "8th Workshop on Mining and Learning with Graphs (with SIGKDD)",
      Month = "July",
      Year = "2010",
      customlinkgithubcode = "https://github.com/zakimjz/SCPM",
      customlinkdata = "https://zenodo.org/record/3732153",
      KeyWord = "Conference,GraphMining,Clustering"
  }
  

• Saeed Salem, Khedidja Seridi, Loqmane Seridi, Jianfei Wu, and Mohammed J. Zaki. Voknn: voting-based nearest neighbor approach for scalable svm training. In 2nd Workshop on Large-scale Data Mining: Theory and Applications (with SIGKDD). July 2010.
  [full text] [BibTeX▼]

  @inproceedings{2010-LDMTA,
      Author = "Salem, Saeed and Seridi, Khedidja and Seridi, Loqmane and Wu, Jianfei and Zaki, Mohammed J.",
      Title = "VOKNN: Voting-based Nearest Neighbor Approach for Scalable SVM Training",
      Booktitle = "2nd Workshop on Large-scale Data Mining: Theory and Applications (with SIGKDD)",
      Month = "July",
      Year = "2010",
      KeyWord = "Conference,Classification"
  }
  

• Jun Huan, Jake Y. Chen, and Mohammed J. Zaki, editors. Proceedings of BIOKDD10: ACM SIGKDD International Workshop on Data Mining in Bioinformatics. Selected papers available as special issue of BMC Bioinformatics (http://www.biomedcentral.com/bmcbioinformatics/supplements/12/S12), 2010. URL: http://www.cs.rpi.edu/~zaki/Workshops/BIOKDD10/.
  [full text] [BibTeX▼]

  @proceedings{2010-biokdd,
      Author = "Huan, Jun and Chen, Jake Y. and Zaki, Mohammed J.",
      Editor = "Huan, Jun and Chen, Jake Y. and Zaki, Mohammed J.",
      Title = "Proceedings of BIOKDD10: ACM SIGKDD International Workshop on Data Mining in Bioinformatics. Selected papers available as special issue of BMC Bioinformatics (http://www.biomedcentral.com/bmcbioinformatics/supplements/12/S12)",
      Year = "2010",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/BIOKDD10/",
      KeyWord = "Book,Biokdd"
  }
  

• Hilmi Yildirim, Vineet Chaoji, and Mohammed J. Zaki. Grail: scalable reachability index for large graphs. Proceedings of the VLDB Endowment (36th International Conference on Very Large Data Bases), 3(1):276–284, 2010.
  [full text] [githubcode] [data] [BibTeX▼]

  @article{2010-grail,
      Author = "Yildirim, Hilmi and Chaoji, Vineet and Zaki, Mohammed J.",
      Title = "GRAIL: Scalable Reachability Index for Large Graphs",
      Journal = "Proceedings of the VLDB Endowment (36th International Conference on Very Large Data Bases)",
      Volume = "3",
      Number = "1",
      Pages = "276-284",
      Year = "2010",
      customlinkgithubcode = "https://github.com/zakimjz/grail",
      customlinkdata = "https://github.com/zakimjz/grail/tree/master/Downloads",
      KeyWord = "Journal,GraphIndexing,Indexing"
  }
  

• Neha Goel, Michael S. Hsiao, Naren Ramakrishnan, and Mohammed J. Zaki. Mining complex boolean expressions for sequential equivalence checking. In Proceedings of the 19th Asian Test Symposium, Shanghai, China. December 2010.
  [full text] [BibTeX▼]

  @inproceedings{2010-ats,
      Author = "Goel, Neha and Hsiao, Michael S. and Ramakrishnan, Naren and Zaki, Mohammed J.",
      Title = "Mining Complex Boolean Expressions for Sequential Equivalence Checking",
      Booktitle = "Proceedings of the 19th Asian Test Symposium, Shanghai, China",
      Month = "December",
      Year = "2010",
      KeyWord = "Conference,DataMining,ItemsetMining,BooleanPatterns"
  }
  

• Mohammed J. Zaki, Naren Ramakrishnan, and Lizhuang Zhao. Mining frequent boolean expressions: application to gene expression and regulatory modeling. International Journal of Knowledge Discovery in Bioinformatics, 1(3):68–96, September 2010. Special issue on Mining Complex Structures in Biology.
  [full text] [githubcode] [BibTeX▼]

  @article{2010-ijkdb,
      Author = "Zaki, Mohammed J. and Ramakrishnan, Naren and Zhao, Lizhuang",
      Title = "Mining Frequent Boolean Expressions: Application to Gene Expression and Regulatory Modeling",
      Journal = "International Journal of Knowledge Discovery in Bioinformatics",
      Volume = "1",
      Number = "3",
      Pages = "68-96",
      Year = "2010",
      Month = "September",
      Note = "Special issue on Mining Complex Structures in Biology",
      customlinkgithubcode = "https://github.com/zakimjz/BLOSOM",
      KeyWord = "Journal,ItemsetMining,GeneExpression"
  }
  

2009

• Naren Ramakrishnan and Mohammed J. Zaki. Redescription mining and applications in bioinformatics. In Jake Chen and Stefano Lonardi, editors, Biological Data Mining. CRC Press, 2009. URL: http://www.crcpress.com/product/isbn/9781420086843.
  [full text] [BibTeX▼]

  @incollection{2009-bdm,
      Author = "Ramakrishnan, Naren and Zaki, Mohammed J.",
      Editor = "Chen, Jake and Lonardi, Stefano",
      Title = "Redescription Mining and Applications in Bioinformatics",
      Booktitle = "Biological Data Mining",
      Publisher = "CRC Press",
      Year = "2009",
      url = "http://www.crcpress.com/product/isbn/9781420086843",
      KeyWord = "Chapter,GeneExpression"
  }
  

• Mohammed J. Zaki. Closed itemset mining and non-redundant association rule mining. In Ling Liu and M. Tamer Ozsu, editors, Encyclopedia of Database Systems. Springer-Verlag, 2009. URL: http://www.springer.com/978-0-387-49616-0.
  [full text] [BibTeX▼]

  @incollection{2009-eds,
      Author = "Zaki, Mohammed J.",
      Editor = "Liu, Ling and Ozsu, M. Tamer",
      Title = "Closed Itemset Mining and Non-redundant Association Rule Mining",
      Booktitle = "Encyclopedia of Database Systems",
      Publisher = "Springer-Verlag",
      Year = "2009",
      url = "http://www.springer.com/978-0-387-49616-0",
      KeyWord = "Chapter,ItemsetMining"
  }
  

• Saeed Salem, Mohammed J. Zaki, and Chris Bystroff. FlexSnap: flexible non-sequential protein structure alignment. In 9th Workshop on Algorithms in Bioinformatics. September 2009. *Best Papers Selection*.
  [full text] [BibTeX▼]

  @inproceedings{2009-flexsnap,
      Author = "Salem, Saeed and Zaki, Mohammed J. and Bystroff, Chris",
      Title = "{FlexSnap}: Flexible Non-Sequential Protein Structure Alignment",
      Booktitle = "9th Workshop on Algorithms in Bioinformatics",
      Month = "September",
      Year = "2009",
      Note = "*Best Papers Selection*",
      KeyWord = "Conference,Proteins"
  }
  

• Igor B. Kuznetsov and Mohammed J. Zaki. Integration of multiple types of genome-wide datasets and analysis of functional relationships among genes in the human genome. Technical Report 09-03, Rensselaer Polytechnic Institute, August 2009.
  [full text] [BibTeX▼]

  @techreport{2009-genomewide,
      Author = "Kuznetsov, Igor B. and Zaki, Mohammed J.",
      Title = "Integration of multiple types of genome-wide datasets and analysis of functional relationships among genes in the human genome",
      Institution = "Rensselaer Polytechnic Institute",
      Number = "09-03",
      Month = "August",
      Year = "2009",
      KeyWord = "TechReport,GeneExpression"
  }
  

• Stephen Kelley, Mark Goldberg, Malik Magdon-Ismail, Konstantin Mertsalov, William Wallace, and Mohammed J. Zaki. Graphont: an ontology based library for conversion from semantic graphs to jung. In 7th IEEE International Conference on Intelligence and Security Informatics. June 2009.
  [full text] [BibTeX▼]

  @inproceedings{2009-graphont,
      Author = "Kelley, Stephen and Goldberg, Mark and Magdon-Ismail, Malik and Mertsalov, Konstantin and Wallace, William and Zaki, Mohammed J.",
      Title = "graphOnt: An Ontology Based Library for Conversion from Semantic Graphs to JUNG",
      Booktitle = "7th IEEE International Conference on Intelligence and Security Informatics",
      Month = "June",
      Year = "2009",
      KeyWord = "Conference,GraphMining"
  }
  

• Mohammad Al Hasan and Mohammed J. Zaki. Output space sampling for graph patterns. Proceedings of the VLDB Endowment (35th International Conference on Very Large Data Bases), 2(1):730–741, 2009.
  [full text] [BibTeX▼]

  @article{2009-graphsampling,
      Author = "Hasan, Mohammad Al and Zaki, Mohammed J.",
      Title = "Output Space Sampling for Graph Patterns",
      Journal = "Proceedings of the VLDB Endowment (35th International Conference on Very Large Data Bases)",
      Volume = "2",
      Number = "1",
      Pages = "730-741",
      Year = "2009",
      KeyWord = "Journal,GraphMining,Sampling"
  }
  

• Adriano Veloso, Mohammed J. Zaki, Jr. Wagner Meira, and Marcos Goncalves. The metric dilemma: competence-conscious associative classification. In 9th SIAM International Conference on Data Mining. April 2009.
  [full text] [BibTeX▼]

  @inproceedings{2009-lac-competence,
      Author = "Veloso, Adriano and Zaki, Mohammed J. and Wagner Meira, Jr. and Goncalves, Marcos",
      Title = "The Metric Dilemma: Competence-Conscious Associative Classification",
      Booktitle = "9th SIAM International Conference on Data Mining",
      Month = "April",
      Year = "2009",
      KeyWord = "Conference,Classification"
  }
  

• Mohammad Al Hasan and Mohammed J. Zaki. Musk: uniform sampling of k maximal patterns. In 9th SIAM International Conference on Data Mining. April 2009. *Best Paper Runner-Up*.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2009-musk,
      Author = "Hasan, Mohammad Al and Zaki, Mohammed J.",
      Title = "MUSK: Uniform Sampling of k Maximal Patterns",
      Booktitle = "9th SIAM International Conference on Data Mining",
      Month = "April",
      Year = "2009",
      Note = "*Best Paper Runner-Up*",
      customlinkgithubcode = "https://github.com/zakimjz/MUSK",
      KeyWord = "Conference,GraphMining,Sampling"
  }
  

• Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, and Mohammed J. Zaki. Robust partitional clustering by outlier and density insensitive seeding. Pattern Recognition Letters, 30(11):994–1002, August 2009. doi:10.1016/j.patrec.2009.04.013.
  [full text] [BibTeX▼]

  @article{2009-robin:prl,
      Author = "Hasan, Mohammad Al and Chaoji, Vineet and Salem, Saeed and Zaki, Mohammed J.",
      Title = "Robust Partitional Clustering by Outlier and Density Insensitive Seeding",
      Journal = "Pattern Recognition Letters",
      Volume = "30",
      Number = "11",
      Pages = "994-1002",
      Month = "August",
      Year = "2009",
      Doi = "10.1016/j.patrec.2009.04.013",
      KeyWord = "Journal,Clustering"
  }
  

• John Elder, Francoise Soulie Fogelman, Peter Flach, and Mohammed J. Zaki, editors. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, June 2009. ISBN:978-1-60558-495-9. URL: http://portal.acm.org/toc.cfm?id=1557019.
  [BibTeX▼]

  @proceedings{2009-sigkdd,
      Author = "Elder, John and Fogelman, Francoise Soulie and Flach, Peter and Zaki, Mohammed J.",
      Editor = "Elder, John and Fogelman, Francoise Soulie and Flach, Peter and Zaki, Mohammed J.",
      Title = "Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining",
      Organization = "ACM",
      Month = "June",
      Year = "2009",
      Note = "ISBN:978-1-60558-495-9",
      url = "http://portal.acm.org/toc.cfm?id=1557019",
      KeyWord = "Book,DataMining"
  }
  

• Mohammad Al Hasan, Saeed Salem, Benjarath Pupacdi, and Mohammed J. Zaki. Clustering with lower bound on similarity. In 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining. April 2009. *Best Paper Award*.
  [full text] [BibTeX▼]

  @inproceedings{2009-simclus,
      Author = "Hasan, Mohammad Al and Salem, Saeed and Pupacdi, Benjarath and Zaki, Mohammed J.",
      Title = "Clustering with Lower Bound on Similarity",
      Booktitle = "13th Pacific-Asia Conference on Knowledge Discovery and Data Mining",
      Month = "April",
      Year = "2009",
      Note = "*Best Paper Award*",
      KeyWord = "Conference,Clustering,GraphMining"
  }
  

• Saeed Salem, Mohammed J. Zaki, and Chris Bystroff. Iterative non-sequential protein structural alignment. Journal of Bioinformatics and Computational Biology, 7(3):571–596, June 2009. Special issue on the best of CSB'08. doi:10.1142/S0219720009004205.
  [full text] [BibTeX▼]

  @article{2009-snap:jbcb,
      Author = "Salem, Saeed and Zaki, Mohammed J. and Bystroff, Chris",
      Title = "Iterative Non-Sequential Protein Structural Alignment",
      Journal = "Journal of Bioinformatics and Computational Biology",
      Note = "Special issue on the best of CSB'08",
      Volume = "7",
      Number = "3",
      Pages = "571-596",
      Month = "June",
      Year = "2009",
      Pmid = "19507290",
      Doi = "10.1142/S0219720009004205",
      KeyWord = "Journal,Proteins"
  }
  

• Vineet Chaoji, Mohammad Al Hasan, Saeed Salem, and Mohammed J. Zaki. SPARCL: an effective and efficient algorithm for mining arbitrary shape-based clusters. Knowledge and Information Systems, 21(2):201–229, November 2009. Invited: best papers of ICDM'08. doi:10.1007/s10115-009-0216-0.
  [full text] [BibTeX▼]

  @article{2009-sparcl:kais,
      Author = "Chaoji, Vineet and Hasan, Mohammad Al and Salem, Saeed and Zaki, Mohammed J.",
      Title = "{SPARCL}: An Effective and Efficient Algorithm for Mining Arbitrary Shape-based Clusters",
      Journal = "Knowledge and Information Systems",
      Note = "Invited: best papers of ICDM'08",
      Volume = "21",
      Number = "2",
      Pages = "201-229",
      Month = "November",
      Year = "2009",
      Doi = "10.1007/s10115-009-0216-0",
      KeyWord = "Journal,Clustering"
  }
  

• Amanda W. Lund, Cagatay C. Bilgin, Mohammed Al Hasan, Lindsey M. McKeen, Jan P. Stegeman, Bulent Yener, Mohammed J. Zaki, and George E. Plopper. Quantification of spatial parameters in 3d cellular constructs using graph theory. Journal of Biomedicine and Biotechnology, 2009(928286):16, 2009. URL: http://www.hindawi.com/journals/jbb/2009/928286.html, doi:10.1155/2009/928286.
  [full text] [BibTeX▼]

  @article{2009-jbb,
      Author = "Lund, Amanda W. and Bilgin, Cagatay C. and Hasan, Mohammed Al and McKeen, Lindsey M. and Stegeman, Jan P. and Yener, Bulent and Zaki, Mohammed J. and Plopper, George E.",
      Title = "Quantification of Spatial Parameters in 3D Cellular Constructs Using Graph Theory",
      Journal = "Journal of Biomedicine and Biotechnology",
      Volume = "2009",
      Number = "928286",
      Pages = "16",
      Year = "2009",
      Pmid = "19920859",
      url = "http://www.hindawi.com/journals/jbb/2009/928286.html",
      Doi = "10.1155/2009/928286",
      KeyWord = "Journal,Biokdd,CellGraphs"
  }
  

• Amir H. Qureshi, Vineet Chaoji, Dony Maiguel, Hafeez Faridi, Constantinos Barth, Erliang Zheng, Jeremy Besson, Saeed M. Salem, Mudita Singhal, David Sarracino, Bryan Krastins, Mitsunori Ogihara, Mohammed J. Zaki, and Vineet Gupta. Proteomic and phospho-proteomic profile of human platelets in basal, resting state: insights into integrin signaling. PLoS ONE, 4(10):e7627, 2009. doi:10.1371/journal.pone.0007627.
  [full text] [BibTeX▼]

  @article{2009-PLOS,
      Author = "Qureshi, Amir H. and Chaoji, Vineet and Maiguel, Dony and Faridi, Hafeez and Barth, Constantinos and Zheng, Erliang and Besson, Jeremy and Salem, Saeed M. and Singhal, Mudita and Sarracino, David and Krastins, Bryan and Ogihara, Mitsunori and Zaki, Mohammed J. and Gupta, Vineet",
      Title = "Proteomic and Phospho-proteomic Profile of Human Platelets in Basal, Resting State: Insights into Integrin Signaling",
      Journal = "PLoS ONE",
      Volume = "4",
      Number = "10",
      Pages = "e7627",
      Year = "2009",
      Pmid = "19859549",
      doi = "10.1371/journal.pone.0007627",
      KeyWord = "Journal,Biokdd,PPI"
  }
  

• Peter A. Flach, Sebastian Spiegler, Simon Price Bruno Golenia, John Guiver, Ralf Hebrich, Thore Graepel, and Mohammed J. Zaki. Novel tools to streamline the conference review process: experiences from sigkdd'09. SIGKDD Explorations, 11(2):63–67, December 2009. URL: http://www.sigkdd.org/explorations/issue.php?volume=11&issue=2&year=2009&month=12.
  [full text] [BibTeX▼]

  @article{2009-explor,
      Author = "Flach, Peter A. and Spiegler, Sebastian and Bruno Golenia, Simon Price and Guiver, John and Hebrich, Ralf and Graepel, Thore and Zaki, Mohammed J.",
      Title = "Novel Tools to Streamline the Conference Review Process: Experiences from SIGKDD'09",
      Journal = "SIGKDD Explorations",
      Volume = "11",
      Number = "2",
      Pages = "63-67",
      Month = "December",
      Year = "2009",
      url = "http://www.sigkdd.org/explorations/issue.php?volume=11\&issue=2\&year=2009\&month=12",
      KeyWord = "Journal,DataMining"
  }
  

2008

• Mohammed J. Zaki, Naren Ramakrishnan, and Srinivasan Parthasarathy. Editorial: biological data mining. Scientific Programming, 16(1):3, 2008.
  [full text] [BibTeX▼]

  @article{2008-SP,
      Author = "Zaki, Mohammed J. and Ramakrishnan, Naren and Parthasarathy, Srinivasan",
      Title = "Editorial: Biological data mining",
      Journal = "Scientific Programming",
      Volume = "16",
      Number = "1",
      Pages = "3",
      Year = "2008",
      KeyWord = "Journal,GuestEditor,Biokdd"
  }
  

• Stefano Lonardi, Jake Y. Chen, and Mohammed J. Zaki, editors. Proceedings of BIOKDD08: ACM SIGKDD International Workshop on Data Mining in Bioinformatics, 2008. URL: http://www.cs.rpi.edu/~zaki/Workshops/BIOKDD08/.
  [full text] [BibTeX▼]

  @proceedings{2008-biokdd,
      Author = "Lonardi, Stefano and Chen, Jake Y. and Zaki, Mohammed J.",
      Editor = "Lonardi, Stefano and Chen, Jake Y. and Zaki, Mohammed J.",
      Title = "Proceedings of BIOKDD08: ACM SIGKDD International Workshop on Data Mining in Bioinformatics",
      Year = "2008",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/BIOKDD08/",
      KeyWord = "Book,Biokdd"
  }
  

• Zujun Shentu, Mohammad Al Hasan, Chris Bystroff, and Mohammed J. Zaki. Context Shapes: efficient complementary shape matching for protein-protein docking. Proteins: Structure, Function and Bioinformatics, 70(3):1056–1073, February 2008. doi:10.1002/prot.21600.
  [full text] [githubcode] [BibTeX▼]

  @article{2008-contextshapes:proteins,
      Author = "Shentu, Zujun and Hasan, Mohammad Al and Bystroff, Chris and Zaki, Mohammed J.",
      Title = "{Context Shapes}: Efficient Complementary Shape Matching for Protein-Protein Docking",
      Journal = "Proteins: Structure, Function and Bioinformatics",
      Volume = "70",
      Number = "3",
      Pages = "1056-1073",
      Month = "February",
      Year = "2008",
      Pmid = "17847098",
      Doi = "10.1002/prot.21600",
      customlinkgithubcode = "https://github.com/zakimjz/ContextShapes",
      KeyWord = "Journal,Proteins"
  }
  

• Vineet Chaoji, Mohammad Al Hasan, Saeed Salem, and Mohammed J. Zaki. An integrated, generic approach to pattern mining: data mining template library. Data Mining and Knowledge Discovery, 17(3):457–495, December 2008. doi:10.1007/s10618-008-0098-x.
  [full text] [githubcode] [BibTeX▼]

  @article{2008-dmtl:dmkd,
      Author = "Chaoji, Vineet and Hasan, Mohammad Al and Salem, Saeed and Zaki, Mohammed J.",
      Title = "An integrated, generic approach to pattern mining: data mining template library",
      Journal = "Data Mining and Knowledge Discovery",
      Volume = "17",
      Number = "3",
      Pages = "457-495",
      Month = "December",
      Year = "2008",
      Doi = "10.1007/s10618-008-0098-x",
      customlinkgithubcode = "https://github.com/zakimjz/DMTL",
      KeyWord = "Journal,DataMining,GraphMining,TreeMining,SequenceMining,ItemsetMining"
  }
  

• Jake Y. Chen, Mohammed J. Zaki, and Stefano Lonardi. Biokdd08: a workshop report on data mining in bioinformatics. SIGKDD Explorations, 10(2):54–56, December 2008.
  [full text] [BibTeX▼]

  @article{2008-explor,
      Author = "Chen, Jake Y. and Zaki, Mohammed J. and Lonardi, Stefano",
      Title = "BIOKDD08: A Workshop report on Data Mining in Bioinformatics",
      Journal = "SIGKDD Explorations",
      Volume = "10",
      Number = "2",
      Pages = "54-56",
      Month = "December",
      Year = "2008",
      KeyWord = "Journal,Biokdd"
  }
  

• Adriano Veloso, Jr. Wagner Meira, and Mohammed J. Zaki. Calibrated lazy associative classification. In Brazilian Symposium on Databases. October 2008. *Best Paper Runner-Up*.
  [full text] [BibTeX▼]

  @inproceedings{2008-lac-calibrated,
      Author = "Veloso, Adriano and Wagner Meira, Jr. and Zaki, Mohammed J.",
      Title = "Calibrated Lazy Associative Classification",
      Booktitle = "Brazilian Symposium on Databases",
      Month = "October",
      Year = "2008",
      Note = "*Best Paper Runner-Up*",
      KeyWord = "Conference,Classification"
  }
  

• Vineet Chaoji, Mohammad Al Hasan, Saeed Salem, Jeremy Besson, and Mohammed J. Zaki. ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns. Statistical Analysis and Data Mining, 1(2):67–84, June 2008. doi:10.1002/sam.10004.
  [full text] [githubcode] [BibTeX▼]

  @article{2008-origami:sadm,
      Author = "Chaoji, Vineet and Hasan, Mohammad Al and Salem, Saeed and Besson, Jeremy and Zaki, Mohammed J.",
      Title = "{ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns}",
      Journal = "Statistical Analysis and Data Mining",
      Volume = "1",
      Number = "2",
      Pages = "67-84",
      Month = "June",
      Year = "2008",
      Doi = "10.1002/sam.10004",
      customlinkgithubcode = "https://github.com/zakimjz/Origami",
      KeyWord = "Journal,GraphMining,Sampling"
  }
  

• Renata B. Araujo, Guilherme H. T. Ferreira, Gustavo H. Orair, Wagner Meira Jr., Renato A. C. Ferreira, Dorgival O. G. Neto, and Mohammed J. Zaki. The partricluster algorithm for gene expression analysis. International Journal of Parallel Programming, 36(2):226–249, April 2008. Special issue on SBAC-PAD 2006. doi:10.1007/s10766-007-0067-9.
  [full text] [BibTeX▼]

  @article{2008-partricluster-ijpp,
      author = "Araujo, Renata B. and Ferreira, Guilherme H. T. and Orair, Gustavo H. and Jr., Wagner Meira and Ferreira, Renato A. C. and Neto, Dorgival O. G. and Zaki, Mohammed J.",
      title = "The ParTriCluster Algorithm for Gene Expression Analysis",
      journal = "International Journal of Parallel Programming",
      Note = "Special issue on SBAC-PAD 2006",
      volume = "36",
      number = "2",
      pages = "226-249",
      Month = "April",
      year = "2008",
      Doi = "10.1007/s10766-007-0067-9",
      KeyWord = "Journal,ParallelDM,GeneExpression"
  }
  

• Feng Gao and Mohammed J. Zaki. Indexing protein structures using suffix trees. In Mohammed J. Zaki and Chris Bystroff, editors, Protein Structure Prediction, Methods in Molecular Biology 413, chapter 6, pages 147–169. Springer/Humana Press, second edition edition, 2008.
  [full text] [githubcode] [BibTeX▼]

  @incollection{2008-psist,
      Author = "Gao, Feng and Zaki, Mohammed J.",
      Editor = "Zaki, Mohammed J. and Bystroff, Chris",
      Title = "Indexing Protein Structures Using Suffix Trees",
      Booktitle = "Protein Structure Prediction",
      Edition = "Second Edition",
      Series = "Methods in Molecular Biology 413",
      Chapter = "6",
      Pages = "147-169",
      Publisher = "Springer/Humana Press",
      Year = "2008",
      Pmid = "18075165",
      customlinkgithubcode = "https://github.com/zakimjz/PSIST",
      KeyWord = "Chapter,Proteins,SuffixTree,Indexing"
  }
  

• Feng Gao and Mohammed J. Zaki. Psist: a scalable approach to indexing protein structures using suffix trees. Journal of Parallel and Distributed Computing, 68(1):55–63, January 2008. Special issue on Parallel Techniques for Information Extraction. doi:10.1016/j.jpdc.2007.07.008.
  [full text] [githubcode] [BibTeX▼]

  @article{2008-psist:jpdc,
      Author = "Gao, Feng and Zaki, Mohammed J.",
      Title = "PSIST: A Scalable Approach to Indexing Protein Structures using Suffix Trees",
      Journal = "Journal of Parallel and Distributed Computing",
      Note = "Special issue on Parallel Techniques for Information Extraction",
      Volume = "68",
      Number = "1",
      Pages = "55-63",
      Month = "January",
      Year = "2008",
      Doi = "10.1016/j.jpdc.2007.07.008",
      customlinkgithubcode = "https://github.com/zakimjz/PSIST",
      KeyWord = "Journal,Proteins,SuffixTree,Indexing"
  }
  

• Mohammed J. Zaki and Chris Bystroff. Protein Structure Prediction. Methods in Molecular Biology 413. Humana Press/Springer (http://www.springer.com/978-1-58829-752-5), second edition edition, 2008. URL: http://www.cs.rpi.edu/~zaki/Books/prot-book/.
  [BibTeX▼]

  @book{2008-pspbook,
      Author = "Zaki, Mohammed J. and Bystroff, Chris",
      Editor = "Zaki, Mohammed J. and Bystroff, Chris",
      Title = "Protein Structure Prediction",
      Edition = "Second Edition",
      Series = "Methods in Molecular Biology 413",
      Publisher = "Humana Press/Springer (http://www.springer.com/978-1-58829-752-5)",
      Year = "2008",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Books/prot-book/",
      KeyWord = "Book,Proteins"
  }
  

• Mohammed J. Zaki and Ke Wang. Editorial: special issue on the best papers of sdm'08. Statistical Analysis and Data Mining, 1(3):109–110, November 2008. doi:10.1002/sam.10010.
  [full text] [BibTeX▼]

  @article{2008-sadm,
      Author = "Zaki, Mohammed J. and Wang, Ke",
      Title = "Editorial: Special Issue on the Best Papers of SDM'08",
      Journal = "Statistical Analysis and Data Mining",
      Volume = "1",
      Number = "3",
      Pages = "109-110",
      Month = "November",
      Year = "2008",
      Doi = "10.1002/sam.10010",
      KeyWord = "Journal,GuestEditor,DataMining"
  }
  

• Chid Apte, Haesun Park, Ke Wang, and Mohammed J. Zaki, editors. Proceedings of the 2008 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Philadelphia, PA, April 2008. ISBN:978-0-89871-654-2. URL: http://www.siam.org/proceedings/datamining/2008/dm08.php.
  [BibTeX▼]

  @proceedings{2008-sdm,
      Author = "Apte, Chid and Park, Haesun and Wang, Ke and Zaki, Mohammed J.",
      Editor = "Apte, Chid and Park, Haesun and Wang, Ke and Zaki, Mohammed J.",
      Title = "Proceedings of the 2008 SIAM International Conference on Data Mining",
      Organization = "Society for Industrial and Applied Mathematics, Philadelphia, PA",
      Month = "April",
      Year = "2008",
      Note = "ISBN:978-0-89871-654-2",
      url = "http://www.siam.org/proceedings/datamining/2008/dm08.php",
      KeyWord = "Book,DataMining"
  }
  

• Saeed Salem and Mohammed J. Zaki. Iterative non-sequential protein structural alignment. In 7th Annual International Conference on Computational Systems Bioinformatics. August 2008. *Best Papers Selection*.
  [full text] [BibTeX▼]

  @inproceedings{2008-snap,
      Author = "Salem, Saeed and Zaki, Mohammed J.",
      Title = "Iterative Non-Sequential Protein Structural Alignment",
      Booktitle = "7th Annual International Conference on Computational Systems Bioinformatics",
      Month = "August",
      Year = "2008",
      Note = "*Best Papers Selection*",
      Pmid = "19642279",
      KeyWord = "Conference,Proteins"
  }
  

• Vineet Chaoji, Mohammad Al Hasan, Saeed Salem, and Mohammed J. Zaki. SPARCL: efficient and effective shape-based clustering. In 8th IEEE International Conference on Data Mining. December 2008. *Best Papers Selection*.
  [full text] [BibTeX▼]

  @inproceedings{2008-sparcl,
      Author = "Chaoji, Vineet and Hasan, Mohammad Al and Salem, Saeed and Zaki, Mohammed J.",
      Title = "{SPARCL}: Efficient and Effective Shape-based Clustering",
      Booktitle = "8th IEEE International Conference on Data Mining",
      Month = "December",
      Year = "2008",
      Note = "*Best Papers Selection*",
      KeyWord = "Conference,Clustering"
  }
  

• Mohammed J. Zaki, George Karypis, Jiong Yang, and Wei Wang. Introduction to special issue on bioinformatics. ACM Transactions on Knowledge Discovery from Data, March 2008. doi:10.1145/1342320.1342321.
  [full text] [BibTeX▼]

  @article{2008-tkdd-editorial,
      Author = "Zaki, Mohammed J. and Karypis, George and Yang, Jiong and Wang, Wei",
      Title = "Introduction to special issue on bioinformatics",
      Journal = "ACM Transactions on Knowledge Discovery from Data",
      Volume = "2",
      Number = "1",
      Article = "1",
      Month = "March",
      Year = "2008",
      Doi = "10.1145/1342320.1342321",
      KeyWord = "Journal,GuestEditor,Biokdd"
  }
  

• Benjarath Phoophakdee and Mohammed J. Zaki. Trellis+: an effective approach for indexing genome-scale sequences using suffix trees. In 13th Pacific Symposium on Biocomputing. January 2008.
  [full text] [BibTeX▼]

  @inproceedings{2008-trellis+,
      Author = "Phoophakdee, Benjarath and Zaki, Mohammed J.",
      Title = "TRELLIS+: An Effective Approach for Indexing Genome-Scale Sequences using Suffix Trees",
      Booktitle = "13th Pacific Symposium on Biocomputing",
      Month = "January",
      Year = "2008",
      Pmid = "18229678",
      KeyWord = "Conference,Bioseq,SuffixTree,Indexing"
  }
  

2007

• Jake Y. Chen, Stefano Lonardi, and Mohammed J. Zaki, editors. Proceedings of BIOKDD07: ACM SIGKDD International Workshop on Data Mining in Bioinformatics, 2007. URL: http://www.cs.rpi.edu/~zaki/Workshops/BIOKDD07/.
  [full text] [BibTeX▼]

  @proceedings{2007-biokdd,
      Author = "Chen, Jake Y. and Lonardi, Stefano and Zaki, Mohammed J.",
      Editor = "Chen, Jake Y. and Lonardi, Stefano and Zaki, Mohammed J.",
      Title = "Proceedings of BIOKDD07: ACM SIGKDD International Workshop on Data Mining in Bioinformatics",
      Year = "2007",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/BIOKDD07/",
      KeyWord = "Book,Biokdd"
  }
  

• Mohammed J. Zaki, Markus Peters, Ira Assent, and Thomas Seidl. CLICKS: an effective algorithm for mining subspace clusters in categorical datasets. Data and Knowledge Engineering, 60(1):51–70, January 2007. Special issue on Intelligent Data Mining. doi:10.1016/j.datak.2006.01.005.
  [full text] [githubcode] [BibTeX▼]

  @article{2007-clicks:dke,
      Author = "Zaki, Mohammed J. and Peters, Markus and Assent, Ira and Seidl, Thomas",
      Title = "{CLICKS}: An Effective Algorithm for Mining Subspace Clusters in Categorical Datasets",
      Journal = "Data and Knowledge Engineering",
      Note = "Special issue on Intelligent Data Mining",
      Volume = "60",
      Number = "1",
      Pages = "51-70",
      Month = "January",
      Year = "2007",
      Doi = "10.1016/j.datak.2006.01.005",
      customlinkgithubcode = "https://github.com/zakimjz/CLICKS",
      KeyWord = "Journal,Clustering"
  }
  

• Mohammed J. Zaki, George Karypis, and Jiong Yang. Editorial: data mining in bioinformatics. Algorithms for molecular biology, 2007. URL: http://www.almob.org/content/2/1/4, doi:10.1186/1748-7188-2-4.
  [full text] [BibTeX▼]

  @article{2007-dmbeditorial:amb,
      Author = "Zaki, Mohammed J. and Karypis, George and Yang, Jiong",
      Title = "Editorial: Data Mining in Bioinformatics",
      Journal = "Algorithms for molecular biology",
      Volume = "2",
      Number = "4",
      Year = "2007",
      Doi = "10.1186/1748-7188-2-4",
      url = "http://www.almob.org/content/2/1/4",
      Pmid = "17428327",
      KeyWord = "Journal,GuestEditor,Biokdd"
  }
  

• Mohammed J. Zaki, Akifumi Makinouchi, and Shunsuke Uemura. Editorial: special issue on biomedical data engineering. International Journal of Bioinformatics Research and Applications, 3(1):1–3, 2007.
  [full text] [BibTeX▼]

  @article{2007-ijbra,
      Author = "Zaki, Mohammed J. and Makinouchi, Akifumi and Uemura, Shunsuke",
      Title = "Editorial: Special issue on Biomedical Data Engineering",
      Journal = "International Journal of Bioinformatics Research and Applications",
      Volume = "3",
      Number = "1",
      Pages = "1-3",
      Year = "2007",
      KeyWord = "Journal,GuestEditor,Biokdd"
  }
  

• Adriano Veloso, Jr. Wagner Meira, Marcos Golcalves, and Mohammed J. Zaki. Multi-label lazy associative classification. In 11th European Conference on Principles and Practice of Knowledge Discovery. September 2007.
  [full text] [BibTeX▼]

  @inproceedings{2007-lac-multilabel,
      Author = "Veloso, Adriano and Wagner Meira, Jr. and Golcalves, Marcos and Zaki, Mohammed J.",
      Title = "Multi-Label Lazy Associative Classification",
      Booktitle = "11th European Conference on Principles and Practice of Knowledge Discovery",
      Month = "September",
      Year = "2007",
      KeyWord = "Conference,ItemsetMining,Classification"
  }
  

• Mohammad Hasan, Vineet Chaoji, Saeed Salem, Jeremy Besson, and Mohammed J. Zaki. ORIGAMI: mining representative orthogonal graph patterns. In 7th IEEE International Conference on Data Mining. October 2007. *Best Papers Selection*.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2007-origami,
      Author = "Hasan, Mohammad and Chaoji, Vineet and Salem, Saeed and Besson, Jeremy and Zaki, Mohammed J.",
      Title = "{ORIGAMI}: Mining Representative Orthogonal Graph Patterns",
      Booktitle = "7th IEEE International Conference on Data Mining",
      Month = "October",
      Year = "2007",
      customlinkgithubcode = "https://github.com/zakimjz/Origami",
      Note = "*Best Papers Selection*",
      KeyWord = "Conference,GraphMining,Sampling"
  }
  

• Karam Gouda, Mosab Hassaan, and Mohammed J. Zaki. PRISM: a prime-encoding approach for frequent sequence mining. In 7th IEEE International Conference on Data Mining. October 2007.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2007-prism,
      Author = "Gouda, Karam and Hassaan, Mosab and Zaki, Mohammed J.",
      Title = "{PRISM}: A Prime-Encoding Approach for Frequent Sequence Mining",
      Booktitle = "7th IEEE International Conference on Data Mining",
      Month = "October",
      Year = "2007",
      customlinkgithubcode = "https://github.com/zakimjz/PRISM",
      KeyWord = "Conference,SequenceMining"
  }
  

• Karlton Sequeira and Mohammed J. Zaki. Exploring similarities across high-dimensional datasets. In David Taniar, editor, Research and Trends in Data Mining Technologies and Applications, chapter 3, pages 53–85. Idea Group, Inc., 2007.
  [full text] [BibTeX▼]

  @incollection{2007-sim,
      Author = "Sequeira, Karlton and Zaki, Mohammed J.",
      Editor = "Taniar, David",
      Title = "Exploring Similarities across High-dimensional Datasets",
      Booktitle = "Research and Trends in Data Mining Technologies and Applications",
      Chapter = "3",
      Pages = "53-85",
      Publisher = "Idea Group, Inc.",
      Year = "2007",
      KeyWord = "Chapter,Clustering"
  }
  

• Mohammed J. Zaki. A unified approach to rooted tree mining: algorithms and applications. In Larry Holder and Diane Cook, editors, Mining Graph Data, chapter 15, pages 381–410. John Wiley and Sons, Inc., 2007.
  [full text] [BibTeX▼]

  @incollection{2007-sleuth,
      Author = "Zaki, Mohammed J.",
      Editor = "Holder, Larry and Cook, Diane",
      Title = "A Unified Approach to Rooted Tree Mining: Algorithms and Applications",
      Booktitle = "Mining Graph Data",
      Chapter = "15",
      Pages = "381-410",
      Publisher = "John Wiley and Sons, Inc.",
      Year = "2007",
      KeyWord = "Chapter,TreeMining"
  }
  

• Benjarath Phoophakdee and Mohammed J. Zaki. Genome-scale disk-based suffix tree indexing. In ACM SIGMOD International Conference on Management of Data. June 2007.
  [full text] [BibTeX▼]

  @inproceedings{2007-trellis,
      Author = "Phoophakdee, Benjarath and Zaki, Mohammed J.",
      Title = "Genome-scale Disk-based Suffix Tree Indexing",
      Booktitle = "ACM SIGMOD International Conference on Management of Data",
      Month = "June",
      Year = "2007",
      KeyWord = "Conference,Bioseq,SuffixTree,Indexing"
  }
  

• Charu A. Aggarwal, Na Ta, Jianyong Wang, Jianhua Feng, and Mohammed J. Zaki. Xproj: a framework for projected structural clustering of xml documents. In 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2007.
  [full text] [BibTeX▼]

  @inproceedings{2007-xproj,
      Author = "Aggarwal, Charu A. and Ta, Na and Wang, Jianyong and Feng, Jianhua and Zaki, Mohammed J.",
      Title = "XProj: A Framework for Projected Structural Clustering of XML Documents",
      Booktitle = "13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      Month = "August",
      Year = "2007",
      KeyWord = "Conference,Clustering"
  }
  

2006

• Shichao Zhang and Mohammed J. Zaki. Editorial: mining multiple data sources: local pattern analysis. Data Mining and Knowledge Discovery: An International Journal, 12(2-3):121–125, May 2006. doi:10.1007/s10618-006-0041-y.
  [full text] [BibTeX▼]

  @article{2006-DMKD,
      Author = "Zhang, Shichao and Zaki, Mohammed J.",
      Title = "Editorial: Mining Multiple Data Sources: Local Pattern Analysis",
      Journal = "Data Mining and Knowledge Discovery: An International Journal",
      Volume = "12",
      Number = "2-3",
      Pages = "121-125",
      Month = "May",
      Year = "2006",
      Doi = "10.1007/s10618-006-0041-y",
      KeyWord = "Journal,GuestEditor,DataMining"
  }
  

• Mohammed J. Zaki, George Karypis, and Jiong Yang, editors. Proceedings of BIOKDD06: ACM SIGKDD Workshop on Data Mining in Bioinformatics, 2006. URL: http://www.cs.rpi.edu/~zaki/Workshops/BIOKDD06/.
  [full text] [BibTeX▼]

  @proceedings{2006-biokdd,
      Author = "Zaki, Mohammed J. and Karypis, George and Yang, Jiong",
      Editor = "Zaki, Mohammed J. and Karypis, George and Yang, Jiong",
      Title = "Proceedings of BIOKDD06: ACM SIGKDD Workshop on Data Mining in Bioinformatics",
      Year = "2006",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/BIOKDD06/",
      KeyWord = "Book,Biokdd"
  }
  

• Lizhuang Zhao, Mohammed J. Zaki, and Naren Ramakrishnan. Blosom: a framework for mining arbitrary boolean expressions. In 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2006.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2006-blosom,
      Author = "Zhao, Lizhuang and Zaki, Mohammed J. and Ramakrishnan, Naren",
      Title = "BLOSOM: A Framework for Mining Arbitrary Boolean Expressions",
      Booktitle = "12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      Month = "August",
      Year = "2006",
      customlinkgithubcode = "https://github.com/zakimjz/BLOSOM",
      KeyWord = "Conference,ItemsetMining,GeneExpression,BooleanPatterns"
  }
  

• Yongqiang Zhang and Mohammed J. Zaki. Exmotif: efficient structured motif extraction. In 6th SIGKDD Workshop on Data Mining in Bioinformatics. August 2006. *Best Papers Selection*.
  [full text] [BibTeX▼]

  @inproceedings{2006-exmotif,
      Author = "Zhang, Yongqiang and Zaki, Mohammed J.",
      Title = "ExMotif: Efficient structured motif extraction",
      Booktitle = "6th SIGKDD Workshop on Data Mining in Bioinformatics",
      Month = "August",
      Year = "2006",
      Note = "*Best Papers Selection*",
      KeyWord = "Conference,Bioseq,Biokdd"
  }
  

• Yongqiang Zhang and Mohammed J. Zaki. Exmotif: efficient structured motif extraction. Algorithms for molecular biology, November 2006. URL: http://www.almob.org/content/1/1/21, doi:10.1186/1748-7188-1-21.
  [full text] [BibTeX▼]

  @article{2006-exmotif:amb,
      Author = "Zhang, Yongqiang and Zaki, Mohammed J.",
      Title = "EXMOTIF: efficient structured motif extraction",
      Journal = "Algorithms for molecular biology",
      Volume = "1",
      Number = "21",
      Month = "November",
      Year = "2006",
      Pmid = "17109757",
      Doi = "10.1186/1748-7188-1-21",
      url = "http://www.almob.org/content/1/1/21",
      KeyWord = "Journal,Bioseq,SequenceMining"
  }
  

• Mohammed J. Zaki, George Karypis, and Jiong Yang. Biokdd06: data mining in bioinformatics. SIGKDD Explorations, 8(2):78, December 2006.
  [full text] [BibTeX▼]

  @article{2006-explor,
      Author = "Zaki, Mohammed J. and Karypis, George and Yang, Jiong",
      Title = "BIOKDD06: Data Mining in Bioinformatics",
      Journal = "SIGKDD Explorations",
      Volume = "8",
      Number = "2",
      Month = "December",
      Pages = "78",
      Year = "2006",
      KeyWord = "Journal,Biokdd"
  }
  

• Mohammed J. Zaki and Karlton Sequeira. Data mining in computational biology. In Srinivas Aluru, editor, Handbook of Computational Molecular Biology, Computer and Information Science Series, chapter 38, pages 38/1–38/26. Chapman & Hall/CRC Press, 2006.
  [full text] [BibTeX▼]

  @incollection{2006-hcmb,
      Author = "Zaki, Mohammed J. and Sequeira, Karlton",
      Editor = "Aluru, Srinivas",
      Title = "Data Mining in Computational Biology",
      Booktitle = "Handbook of Computational Molecular Biology",
      Series = "Computer and Information Science Series",
      Chapter = "38",
      Pages = "38/1-38/26",
      Publisher = "Chapman \\& Hall/CRC Press",
      Year = "2006",
      KeyWord = "Chapter,Biokdd"
  }
  

• Jeffery Baumes, Mark Goldberg, Mykola Hayvanovych, Malik Magdon-Ismail, William Wallace, and Mohammed J. Zaki. Finding hidden group structure in a stream of communications. In IEEE International Conference on Intelligence and Security Informatics. May 2006. *Best Paper Honorable Mention*.
  [full text] [BibTeX▼]

  @inproceedings{2006-isi,
      Author = "Baumes, Jeffery and Goldberg, Mark and Hayvanovych, Mykola and Magdon-Ismail, Malik and Wallace, William and Zaki, Mohammed J.",
      Title = "Finding Hidden Group Structure in a Stream of Communications",
      Booktitle = "IEEE International Conference on Intelligence and Security Informatics",
      Month = "May",
      Year = "2006",
      Note = "*Best Paper Honorable Mention*",
      KeyWord = "Conference,GraphMining"
  }
  

• Gregory Piatetsky-Shapiro, Chabane Djeraba, Lise Getoor, Robert Grossman, Ronen Feldman, and Mohammed J. Zaki. What are the grand challenges for data mining?: KDD-2006 panel report. SIGKDD Explorations, 8(2):70–77, December 2006.
  [full text] [BibTeX▼]

  @article{2006-kddpanel,
      author = "Piatetsky-Shapiro, Gregory and Djeraba, Chabane and Getoor, Lise and Grossman, Robert and Feldman, Ronen and Zaki, Mohammed J.",
      title = "{What are the grand challenges for data mining?: KDD-2006 panel report}",
      journal = "SIGKDD Explorations",
      volume = "8",
      number = "2",
      pages = "70-77",
      Month = "December",
      year = "2006",
      KeyWord = "Journal,DataMining"
  }
  

• Richi Nayak and Mohammed J. Zaki. Knowledge Discovery from XML Documents. Volume 3915 of Lecture Notes in Computer Science. Spring-Verlag, 2006. ISBN: 3-540-33180-8. URL: http://www.springerlink.com/content/l35p30431757/.
  [BibTeX▼]

  @book{2006-kdxml,
      Author = "Nayak, Richi and Zaki, Mohammed J.",
      Editor = "Nayak, Richi and Zaki, Mohammed J.",
      Title = "Knowledge Discovery from XML Documents",
      Series = "Lecture Notes in Computer Science",
      Volume = "3915",
      Publisher = "Spring-Verlag",
      Year = "2006",
      Note = "ISBN: 3-540-33180-8",
      url = "http://www.springerlink.com/content/l35p30431757/",
      KeyWord = "Book,DataMining"
  }
  

• Adriano Veloso, Jr. Wagner Meira, and Mohammed J. Zaki. Lazy Associative Classification. In 6th IEEE International Conference on Data Mining. December 2006.
  [full text] [BibTeX▼]

  @inproceedings{2006-lac,
      Author = "Veloso, Adriano and Wagner Meira, Jr. and Zaki, Mohammed J.",
      Title = "{Lazy Associative Classification}",
      Booktitle = "6th IEEE International Conference on Data Mining",
      Month = "December",
      Year = "2006",
      KeyWord = "Conference,ItemsetMining,Classification"
  }
  

• Adriano Veloso, Jr. Wagner Meira, Marco Cristo, Marcos Golcalves, and Mohammed J. Zaki. Multi-evidence, multi-criteria, lazy associative document classification. In 15th ACM International Conference on Information and Knowledge Management. November 2006.
  [full text] [BibTeX▼]

  @inproceedings{2006-lac-multievidence,
      Author = "Veloso, Adriano and Wagner Meira, Jr. and Cristo, Marco and Golcalves, Marcos and Zaki, Mohammed J.",
      Title = "Multi-Evidence, Multi-Criteria, Lazy Associative Document Classification",
      Booktitle = "15th ACM International Conference on Information and Knowledge Management",
      Month = "November",
      Year = "2006",
      KeyWord = "Conference,ItemsetMining,Classification"
  }
  

• Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, and Mohammed J. Zaki. Link prediction using supervised learning. In Workshop on Link Analysis, Counter-terrorism and Security (with SDM). April 2006.
  [full text] [BibTeX▼]

  @inproceedings{2006-linkprediction,
      Author = "Hasan, Mohammad Al and Chaoji, Vineet and Salem, Saeed and Zaki, Mohammed J.",
      Title = "Link Prediction using Supervised Learning",
      Booktitle = "Workshop on Link Analysis, Counter-terrorism and Security (with SDM)",
      Month = "April",
      Year = "2006",
      KeyWord = "Conference,SocialNetworks,Classification,GraphMining"
  }
  

• Yongqiang Zhang and Mohammed J. Zaki. Smotif: efficient structured pattern and profile motif search. Algorithms for molecular biology, November 2006. URL: http://www.almob.org/content/1/1/22, doi:10.1186/1748-7188-1-22.
  [full text] [BibTeX▼]

  @article{2006-smotif:amb,
      Author = "Zhang, Yongqiang and Zaki, Mohammed J.",
      Title = "SMOTIF: efficient structured pattern and profile motif search",
      Journal = "Algorithms for molecular biology",
      Volume = "1",
      Number = "22",
      Month = "November",
      Year = "2006",
      Pmid = "17118189",
      Doi = "10.1186/1748-7188-1-22",
      url = "http://www.almob.org/content/1/1/22",
      KeyWord = "Journal,Bioseq"
  }
  

• Lane Hemaspaandra, Mitsunori Ogihara, Mohammed J. Zaki, and Marius Zimand. The complexity of finding top-toda-equivalence-class members. Theory of Computing Systems, 39(5):669–684, September 2006. doi:10.1007/s00224-005-1211-9.
  [full text] [BibTeX▼]

  @article{2006-toda:tcs,
      Author = "Hemaspaandra, Lane and Ogihara, Mitsunori and Zaki, Mohammed J. and Zimand, Marius",
      Title = "The Complexity of Finding Top-Toda-Equivalence-Class Members",
      Journal = "Theory of Computing Systems",
      Volume = "39",
      Number = "5",
      Pages = "669-684",
      Month = "September",
      Year = "2006",
      Doi = "10.1007/s00224-005-1211-9",
      KeyWord = "Journal,Theory"
  }
  

• Bouchra Bouqata, Christopher D. Carothers, Boleslaw K. Szymanski, and Mohammed J. Zaki. VOGUE: a novel variable order-gap state machine for modeling sequences. In 10th European Conference on Principles and Practice of Knowledge Discovery. September 2006.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2006-vogue,
      Author = "Bouqata, Bouchra and Carothers, Christopher D. and Szymanski, Boleslaw K. and Zaki, Mohammed J.",
      Title = "{VOGUE}: A Novel Variable Order-Gap State Machine for Modeling Sequences",
      Booktitle = "10th European Conference on Principles and Practice of Knowledge Discovery",
      Month = "September",
      Year = "2006",
      customlinkgithubcode = "https://github.com/zakimjz/VOGUE",
      KeyWord = "Conference,HMM,Bioseq,SequenceMining"
  }
  

• Mohammed J. Zaki and Charu C. Aggarwal. Xrules: an effective structural classifier for xml data. Machine Learning Journal, 62(1-2):137–170, February 2006. Special issue on Statistical Relational Learning and Multi-Relational Data Mining. doi:10.1007/s10994-006-5832-2.
  [full text] [githubcode] [BibTeX▼]

  @article{2006-xrules:mlj,
      Author = "Zaki, Mohammed J. and Aggarwal, Charu C.",
      Title = "Xrules: An effective structural classifier for XML data",
      Journal = "Machine Learning Journal",
      Note = "Special issue on Statistical Relational Learning and Multi-Relational Data Mining",
      Volume = "62",
      Number = "1-2",
      Pages = "137-170",
      Month = "February",
      Year = "2006",
      Doi = "10.1007/s10994-006-5832-2",
      customlinkgithubcode = "https://github.com/zakimjz/Xrules",
      KeyWord = "Journal,TreeMining,Classification"
  }
  

2005

• Shunsuke Uemura, Akifumi Makinouchi, and Mohammed J. Zaki, editors. Proceedings of the IEEE International Workshop on Biomedical Data Engineering (BDME:with ICDE), IEEE Xplore Digital Library, April 2005. URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=34556&isYear=2005.
  [BibTeX▼]

  @proceedings{2005-bdme,
      Author = "Uemura, Shunsuke and Makinouchi, Akifumi and Zaki, Mohammed J.",
      Editor = "Uemura, Shunsuke and Makinouchi, Akifumi and Zaki, Mohammed J.",
      Title = "Proceedings of the IEEE International Workshop on Biomedical Data Engineering (BDME:with ICDE)",
      Publisher = "IEEE Xplore Digital Library",
      Month = "April",
      Year = "2005",
      Url = "http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=34556\&isYear=2005",
      KeyWord = "Book,Biokdd"
  }
  

• Srinivasan Parthasarathy, Wei Wang, and Mohammed J. Zaki, editors. Proceedings of BIOKDD05: ACM SIGKDD International Workshop on Data Mining in Bioinformatics, ACM Digital Library (http://portal.acm.org/toc.cfm?id=1134030), 2005. ISBN:1-59593-213-5. URL: http://www.cs.rpi.edu/~zaki/Workshops/BIOKDD05/.
  [full text] [BibTeX▼]

  @proceedings{2005-biokdd,
      Author = "Parthasarathy, Srinivasan and Wang, Wei and Zaki, Mohammed J.",
      Editor = "Parthasarathy, Srinivasan and Wang, Wei and Zaki, Mohammed J.",
      Title = "Proceedings of BIOKDD05: ACM SIGKDD International Workshop on Data Mining in Bioinformatics",
      Year = "2005",
      Publisher = "ACM Digital Library (http://portal.acm.org/toc.cfm?id=1134030)",
      Note = "ISBN:1-59593-213-5",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/BIOKDD05/",
      KeyWord = "Book,Biokdd"
  }
  

• Mohammed J. Zaki and Ching-Jui Hsiao. Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Transactions on Knowledge and Data Engineering, 17(4):462–478, April 2005. doi:10.1109/69.846291.
  [full text] [CHARMcode] [CHARM-Lcode] [BibTeX▼]

  @article{2005-charm:tkde,
      Author = "Zaki, Mohammed J. and Hsiao, Ching-Jui",
      Title = "Efficient Algorithms for Mining Closed Itemsets and their Lattice Structure",
      Journal = "IEEE Transactions on Knowledge and Data Engineering",
      Volume = "17",
      Number = "4",
      Pages = "462-478",
      Month = "April",
      Year = "2005",
      Doi = "10.1109/69.846291",
      customlinkCHARMcode = "https://github.com/zakimjz/CHARM",
      customlinkCHARM-Lcode = "https://github.com/zakimjz/CHARM-L",
      KeyWord = "Journal,ItemsetMining"
  }
  

• Mohammed J. Zaki, Markus Peters, Ira Assent, and Thomas Seidl. CLICKS: an effective algorithm for mining subspace clusters in categorical datasets. In 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2005.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2005-clicks,
      Author = "Zaki, Mohammed J. and Peters, Markus and Assent, Ira and Seidl, Thomas",
      Title = "{CLICKS}: An Effective Algorithm for Mining Subspace Clusters in Categorical Datasets",
      Booktitle = "11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      Month = "August",
      Year = "2005",
      customlinkgithubcode = "https://github.com/zakimjz/CLICKS",
      KeyWord = "Conference,Clustering"
  }
  

• Markus Peters and Mohammed J. Zaki. CLICKS: clustering categorical data using k-partite maximal cliques. In 21st IEEE International Conference on Data Engineering. April 2005.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2005-clicks:icde,
      Author = "Peters, Markus and Zaki, Mohammed J.",
      Title = "{CLICKS}: clustering categorical data using K-partite maximal cliques",
      Booktitle = "21st IEEE International Conference on Data Engineering",
      Month = "April",
      Year = "2005",
      customlinkgithubcode = "https://github.com/zakimjz/CLICKS",
      KeyWord = "Conference,Clustering"
  }
  

• Jason T. L. Wang, Mohammed J. Zaki, Hannu T. T. Toivonen, and Dennis Shasha. Data Mining in Bioinformatics. Springer-Verlag London, UK (http://www.springer.com/978-1-85233-671-4), 2005. URL: http://www.cs.rpi.edu/~zaki/Books/biokdd-book/.
  [BibTeX▼]

  @book{2005-dmbbook,
      Author = "Wang, Jason T. L. and Zaki, Mohammed J. and Toivonen, Hannu T. T. and Shasha, Dennis",
      Editor = "Wang, Jason T. L. and Zaki, Mohammed J. and Toivonen, Hannu T. T. and Shasha, Dennis",
      Title = "Data Mining in Bioinformatics",
      Publisher = "Springer-Verlag London, UK (http://www.springer.com/978-1-85233-671-4)",
      Year = "2005",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Books/biokdd-book/",
      KeyWord = "Book,Biokdd"
  }
  

• Mohammed J. Zaki, Nilanjana De, Feng Gao, Paolo Palmerini, Nagender Parimi, Jeevan Pathuri, Benjarath Phoophakdee, and Joe Urban. Generic pattern mining via data mining template library. In Heikki Mannila Jean-Francois Boulicaut, Luc De Raedt, editor, Constraint-Based Mining, volume 3848 of LNAI State-of-the-Art Survey, pages 362–279. Springer-Verlag, 2005.
  [full text] [BibTeX▼]

  @incollection{2005-dmtl-cbm,
      Author = "Zaki, Mohammed J. and De, Nilanjana and Gao, Feng and Palmerini, Paolo and Parimi, Nagender and Pathuri, Jeevan and Phoophakdee, Benjarath and Urban, Joe",
      Editor = "Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila",
      Title = "Generic Pattern Mining via Data Mining Template Library",
      Booktitle = "Constraint-Based Mining",
      Series = "LNAI State-of-the-Art Survey",
      Volume = "3848",
      Pages = "362-279",
      Publisher = "Springer-Verlag",
      Year = "2005",
      KeyWord = "Chapter,DataMining,GraphMining,SequenceMining,ItemsetMining"
  }
  

• Mohammed J. Zaki, Nagender Parimi, Nilanjana De, Feng Gao, Benjarath Phoophakdee, Joe Urban, Vineet Chaoji, Mohammad Al Hasan, and Saeed Salem. Towards generic pattern mining (invited paper). In International Conference on Formal Concept Analysis. February 2005.
  [full text] [BibTeX▼]

  @inproceedings{2005-dmtl-icfca,
      Author = "Zaki, Mohammed J. and Parimi, Nagender and De, Nilanjana and Gao, Feng and Phoophakdee, Benjarath and Urban, Joe and Chaoji, Vineet and Hasan, Mohammad Al and Salem, Saeed",
      Title = "Towards Generic Pattern Mining (Invited Paper)",
      Booktitle = "International Conference on Formal Concept Analysis",
      Month = "February",
      Year = "2005",
      KeyWord = "Conference,DataMining"
  }
  

• Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, Nagender Parimi, and Mohammed J. Zaki. DMTL: a generic data mining template library. In Workshop on Library-Centric Software Design (with OOPSLA). October 2005.
  [full text] [BibTeX▼]

  @inproceedings{2005-dmtl-lcsd,
      Author = "Hasan, Mohammad Al and Chaoji, Vineet and Salem, Saeed and Parimi, Nagender and Zaki, Mohammed J.",
      Title = "{DMTL}: A Generic Data Mining Template Library",
      Booktitle = "Workshop on Library-Centric Software Design (with OOPSLA)",
      Month = "October",
      Year = "2005",
      KeyWord = "Conference,DataMining,GraphMining,SequenceMining,ItemsetMining"
  }
  

• Mohammed J. Zaki, Nilanjana De, Feng Gao, Nagender Parimi, Benjarath Phoophakdee, Joe Urban, Vineet Chaoji, Mohammad Al Hasan, and Saeed Salem. Towards generic pattern mining (extended abstract). In 1st International Conference on Pattern Recognition and Machine Intelligence. December 2005.
  [full text] [BibTeX▼]

  @inproceedings{2005-dmtl-premi,
      Author = "Zaki, Mohammed J. and De, Nilanjana and Gao, Feng and Parimi, Nagender and Phoophakdee, Benjarath and Urban, Joe and Chaoji, Vineet and Hasan, Mohammad Al and Salem, Saeed",
      Title = "Towards Generic Pattern Mining (Extended Abstract)",
      Booktitle = "1st International Conference on Pattern Recognition and Machine Intelligence",
      Month = "December",
      Year = "2005",
      KeyWord = "Conference,DataMining"
  }
  

• Srinivasan Parthasarathy, Wei Wang, and Mohammed J. Zaki. Biokdd 2005 workshop report. SIGKDD Explorations, 7(2):129–131, December 2005.
  [full text] [BibTeX▼]

  @article{2005-explor-biokdd,
      Author = "Parthasarathy, Srinivasan and Wang, Wei and Zaki, Mohammed J.",
      Title = "BIOKDD 2005 Workshop Report",
      Journal = "SIGKDD Explorations",
      Volume = "7",
      Number = "2",
      Month = "December",
      Pages = "129-131",
      Year = "2005",
      KeyWord = "Journal,Biokdd"
  }
  

• Bart Goethals, Siegfried Nijssen, and Mohammed J. Zaki. Open source data mining: workshop report. SIGKDD Explorations, 7(2):143–144, December 2005.
  [full text] [BibTeX▼]

  @article{2005-explor-osdm,
      Author = "Goethals, Bart and Nijssen, Siegfried and Zaki, Mohammed J.",
      Title = "Open Source Data Mining: Workshop Report",
      Journal = "SIGKDD Explorations",
      Volume = "7",
      Number = "2",
      Month = "December",
      Pages = "143-144",
      Year = "2005",
      KeyWord = "Journal,ItemsetMining"
  }
  

• Karam Gouda and Mohammed J. Zaki. Genmax: an efficient algorithm for mining maximal frequent itemsets. Data Mining and Knowledge Discovery: An International Journal, 11(3):223–242, November 2005. doi:10.1007/s10618-005-0002-x.
  [full text] [githubcode] [BibTeX▼]

  @article{2005-genmax:dmkd,
      Author = "Gouda, Karam and Zaki, Mohammed J.",
      Title = "GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets",
      Journal = "Data Mining and Knowledge Discovery: An International Journal",
      Volume = "11",
      Number = "3",
      Pages = "223-242",
      Month = "November",
      Year = "2005",
      Doi = "10.1007/s10618-005-0002-x",
      customlinkgithubcode = "https://github.com/zakimjz/Genmax",
      KeyWord = "Journal,ItemsetMining"
  }
  

• Ganesh Ramesh, Mohammed J. Zaki, and William A. Maniatty. Distribution-based synthetic database generation techniques for itemset mining. In 9th International Database Engineering and Applications Symposium. July 2005.
  [full text] [BibTeX▼]

  @inproceedings{2005-ideas,
      Author = "Ramesh, Ganesh and Zaki, Mohammed J. and Maniatty, William A.",
      Title = "Distribution-Based Synthetic Database Generation Techniques for Itemset Mining",
      Booktitle = "9th International Database Engineering and Applications Symposium",
      Month = "July",
      Year = "2005",
      KeyWord = "Conference,ItemsetMining"
  }
  

• Mohammed J. Zaki. Mining maximal and closed frequent itemsets. In Mehmet Kantardzic and Jozef Zurada, editors, New Generation of Data Mining Applications, chapter 23, pages 571–598. IEEE/Wiley Press, 2005.
  [full text] [BibTeX▼]

  @incollection{2005-maxclosed,
      Author = "Zaki, Mohammed J.",
      Editor = "Kantardzic, Mehmet and Zurada, Jozef",
      Title = "Mining Maximal and Closed Frequent Itemsets",
      Booktitle = "New Generation of Data Mining Applications",
      Chapter = "23",
      Pages = "571-598",
      Publisher = "IEEE/Wiley Press",
      Year = "2005",
      KeyWord = "Chapter,ItemsetMining"
  }
  

• Lizhuang Zhao and Mohammed J. Zaki. Microcluster: an efficient deterministic biclustering algorithm for microarray data. IEEE Intelligent Systems, 20(6):40–49, Nov/Dec 2005. Special issue on Data Mining for Bioinformatics.
  [full text] [githubcode] [BibTeX▼]

  @article{2005-microcluster:is,
      Author = "Zhao, Lizhuang and Zaki, Mohammed J.",
      Title = "MicroCluster: An Efficient Deterministic Biclustering Algorithm for Microarray Data",
      Journal = "IEEE Intelligent Systems",
      Note = "Special issue on Data Mining for Bioinformatics",
      Volume = "20",
      Number = "6",
      Pages = "40-49",
      Month = "Nov/Dec",
      Year = "2005",
      customlinkgithubcode = "https://github.com/zakimjz/TriCluster",
      KeyWord = "Journal,GeneExpression"
  }
  

• Bart Goethals, Siegfried Nijssen, and Mohammed J. Zaki, editors. Proceedings of the 1st ACM SIGKDD International workshop on open source data mining: frequent pattern mining implementations, ACM Digital Library (http://portal.acm.org/toc.cfm?id=1133905), 2005. ISBN:1-59593-210-0. URL: http://www.cs.rpi.edu/~zaki/Workshops/OSDM05.
  [full text] [BibTeX▼]

  @proceedings{2005-osdm,
      Author = "Goethals, Bart and Nijssen, Siegfried and Zaki, Mohammed J.",
      Editor = "Goethals, Bart and Nijssen, Siegfried and Zaki, Mohammed J.",
      Title = "Proceedings of the 1st ACM SIGKDD International workshop on open source data mining: frequent pattern mining implementations",
      Year = "2005",
      Publisher = "ACM Digital Library (http://portal.acm.org/toc.cfm?id=1133905)",
      Note = "ISBN:1-59593-210-0",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/OSDM05",
      KeyWord = "Book,ItemsetMining"
  }
  

• Mohammed J. Zaki, Vinay Nadimpally, Deb Bardhan, and Chris Bystroff. Predicting protein folding pathways. In Jason Wang, Mohammed Zaki, Hannu Toivonen, and Dennis Shasha, editors, Data Mining in Bioinformatics, pages 127–141. Springer-Verlag London Ltd., 2005.
  [full text] [BibTeX▼]

  @incollection{2005-pathways,
      Author = "Zaki, Mohammed J. and Nadimpally, Vinay and Bardhan, Deb and Bystroff, Chris",
      Editor = "Wang, Jason and Zaki, Mohammed and Toivonen, Hannu and Shasha, Dennis",
      Title = "Predicting Protein Folding Pathways",
      Booktitle = "Data Mining in Bioinformatics",
      Pages = "127-141",
      Publisher = "Springer-Verlag London Ltd.",
      Year = "2005",
      KeyWord = "Chapter,Proteins"
  }
  

• Feng Gao and Mohammed J. Zaki. PSIST: indexing protein structures using suffix trees. In IEEE Computational Systems Bioinformatics Conference. August 2005.
  [full text] [BibTeX▼]

  @inproceedings{2005-psist,
      Author = "Gao, Feng and Zaki, Mohammed J.",
      Title = "{PSIST}: Indexing Protein Structures using Suffix Trees",
      Booktitle = "IEEE Computational Systems Bioinformatics Conference",
      Month = "August",
      Year = "2005",
      Pmid = "16447979",
      KeyWord = "Conference,Proteins,SuffixTree,Indexing"
  }
  

• Mohammed J. Zaki and Naren Ramakrishnan. Reasoning about sets using redescription mining. In 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2005.
  [full text] [BibTeX▼]

  @inproceedings{2005-redescription,
      Author = "Zaki, Mohammed J. and Ramakrishnan, Naren",
      Title = "Reasoning about Sets using Redescription Mining",
      Booktitle = "11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      Month = "August",
      Year = "2005",
      KeyWord = "Conference,ItemsetMining"
  }
  

• Karlton Sequeira and Mohammed J. Zaki. SCHISM: a new approach to interesting subspace mining. International Journal of Business Intelligence and Data Mining, 1(2):137–160, 2005. doi:10.1504/IJBIDM.2005.008360.
  [full text] [githubcode] [BibTeX▼]

  @article{2005-schism:ijbidm,
      Author = "Sequeira, Karlton and Zaki, Mohammed J.",
      Title = "{SCHISM}: A New Approach to Interesting Subspace Mining",
      Journal = "International Journal of Business Intelligence and Data Mining",
      Volume = "1",
      Number = "2",
      Pages = "137-160",
      Year = "2005",
      Doi = "10.1504/IJBIDM.2005.008360",
      customlinkgithubcode = "https://github.com/zakimjz/SCHISM",
      KeyWord = "Journal,Clustering"
  }
  

• Mohammed J. Zaki. Efficiently mining frequent embedded unordered trees. Fundamenta Informaticae, 66(1-2):33–52, Mar/Apr 2005. Special issue on Advances in Mining Graphs, Trees and Sequences.
  [full text] [githubcode] [BibTeX▼]

  @article{2005-sleuth:fi,
      Author = "Zaki, Mohammed J.",
      Title = "Efficiently Mining Frequent Embedded Unordered Trees",
      Journal = "Fundamenta Informaticae",
      Note = "Special issue on Advances in Mining Graphs, Trees and Sequences",
      Volume = "66",
      Number = "1-2",
      Pages = "33-52",
      Month = "Mar/Apr",
      Year = "2005",
      customlinkgithubcode = "https://github.com/zakimjz/SLEUTH",
      KeyWord = "Journal,TreeMining"
  }
  

• Mohammed J. Zaki. Efficiently mining frequent trees in a forest: algorithms and applications. IEEE Transactions on Knowledge and Data Engineering, 17(8):1021–1035, August 2005. Special issue on Mining Biological Data. doi:10.1109/TKDE.2005.125.
  [full text] [githubcode] [BibTeX▼]

  @article{2005-treeminer:tkde,
      Author = "Zaki, Mohammed J.",
      Title = "Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications",
      Journal = "IEEE Transactions on Knowledge and Data Engineering",
      Note = "Special issue on Mining Biological Data",
      Volume = "17",
      Number = "8",
      Pages = "1021-1035",
      Month = "August",
      Year = "2005",
      Doi = "10.1109/TKDE.2005.125",
      customlinkgithubcode = "https://github.com/zakimjz/TreeMiner",
      KeyWord = "Journal,TreeMining"
  }
  

• Lizhuang Zhao and Mohammed J. Zaki. TriCluster: an effective algorithm for mining coherent clusters in 3d microarray data. In ACM SIGMOD Conference on Management of Data. June 2005.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2005-tricluster,
      Author = "Zhao, Lizhuang and Zaki, Mohammed J.",
      Booktitle = "ACM SIGMOD Conference on Management of Data",
      Title = "{TriCluster}: An Effective Algorithm for Mining Coherent Clusters in 3D Microarray Data",
      Month = "June",
      Year = "2005",
      customlinkgithubcode = "https://github.com/zakimjz/TriCluster",
      KeyWord = "Conference,Clustering,GeneExpression"
  }
  

2004

• Mohammed J. Zaki, Shinichi Morishita, and Isidore Rigoutsos, editors. Proceedings of BIOKDD04: ACM SIGKDD International Workshop on Data Mining in Bioinformatics, 2004. URL: http://www.cs.rpi.edu/~zaki/Workshops/BIOKDD04/.
  [full text] [BibTeX▼]

  @proceedings{2004-biokdd,
      Author = "Zaki, Mohammed J. and Morishita, Shinichi and Rigoutsos, Isidore",
      Editor = "Zaki, Mohammed J. and Morishita, Shinichi and Rigoutsos, Isidore",
      Title = "Proceedings of BIOKDD04: ACM SIGKDD International Workshop on Data Mining in Bioinformatics",
      Year = "2004",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/BIOKDD04/",
      KeyWord = "Book,Biokdd"
  }
  

• Mohammed J. Zaki, Jingjing Hu, and Chris Bystroff. Methods for mining protein contact maps. In K. Sivakumar H. Kargupta, A. Joshi and Y. Yesha, editors, Data Mining: Next Generation Challenges and Future Directions, chapter 16, pages 291–314. AAAI/MIT Press, 2004.
  [full text] [BibTeX▼]

  @incollection{2004-contactmaps,
      Author = "Zaki, Mohammed J. and Hu, Jingjing and Bystroff, Chris",
      Editor = "H. Kargupta, A. Joshi, K. Sivakumar and Yesha, Y.",
      Title = "Methods for Mining Protein Contact Maps",
      Booktitle = "Data Mining: Next Generation Challenges and Future Directions",
      Chapter = "16",
      Pages = "291-314",
      Publisher = "AAAI/MIT Press",
      Year = "2004",
      KeyWord = "Chapter,Proteins,Biokdd"
  }
  

• Mohammed J. Zaki, Shinichi Morishita, and Isidore Rigoutsos. Report on biokdd04: workshop on data mining in bioinformatics. SIGKDD Explorations, 6(2):153–154, December 2004.
  [full text] [BibTeX▼]

  @article{2004-explor:biokdd,
      Author = "Zaki, Mohammed J. and Morishita, Shinichi and Rigoutsos, Isidore",
      Title = "Report on BIOKDD04: Workshop on Data Mining in Bioinformatics",
      Journal = "SIGKDD Explorations",
      Volume = "6",
      Number = "2",
      Month = "December",
      Pages = "153-154",
      Year = "2004",
      KeyWord = "Journal,Biokdd"
  }
  

• Bart Goethals and Mohammed J. Zaki. Advances in frequent itemset mining implementations: report on FIMI'03. SIGKDD Explorations, 6(1):109–117, June 2004.
  [full text] [BibTeX▼]

  @article{2004-explor:fimi,
      Author = "Goethals, Bart and Zaki, Mohammed J.",
      Title = "Advances in Frequent Itemset Mining Implementations: report on {FIMI'03}",
      Journal = "SIGKDD Explorations",
      Volume = "6",
      Number = "1",
      Pages = "109-117",
      Month = "June",
      Year = "2004",
      KeyWord = "Journal,ItemsetMining"
  }
  

• Roberto Bayardo, Bart Goethals, and Mohammed J. Zaki, editors. Proceedings of the 2nd IEEE ICDM Workshop on Frequent Itemset Mining Implementations, CEUR Workshop Proceedings, 2004. URL: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-126/.
  [full text] [BibTeX▼]

  @proceedings{2004-fimi,
      Author = "Bayardo, Roberto and Goethals, Bart and Zaki, Mohammed J.",
      Editor = "Bayardo, Roberto and Goethals, Bart and Zaki, Mohammed J.",
      Title = "Proceedings of the 2nd IEEE ICDM Workshop on Frequent Itemset Mining Implementations",
      Year = "2004",
      Publisher = "CEUR Workshop Proceedings",
      url = "http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-126/",
      KeyWord = "Book,ItemsetMining"
  }
  

• Mohammed J. Zaki. Data mining. In William S. Bainbridge, editor, The Encyclopedia of Human-Computer Interaction, pages 149–152. Berkshire Publishing Group, 2004.
  [full text] [BibTeX▼]

  @incollection{2004-hcibook,
      Author = "Zaki, Mohammed J.",
      Editor = "Bainbridge, William S.",
      Title = "Data Mining",
      Booktitle = "The Encyclopedia of Human-Computer Interaction",
      Pages = "149-152",
      Publisher = "Berkshire Publishing Group",
      Year = "2004",
      KeyWord = "Chapter,DataMining"
  }
  

• Lane Hemaspaandra, Mitsunori Ogihara, Mohammed J. Zaki, and Marius Zimand. The complexity of finding top-toda-equivalence-class members. In Latin American Theoretical Informatics Conference. April 2004.
  [full text] [BibTeX▼]

  @inproceedings{2004-latin,
      Author = "Hemaspaandra, Lane and Ogihara, Mitsunori and Zaki, Mohammed J. and Zimand, Marius",
      Title = "The Complexity of Finding Top-Toda-Equivalence-Class Members",
      Booktitle = "Latin American Theoretical Informatics Conference",
      Month = "April",
      Year = "2004",
      KeyWord = "Conference,Theory"
  }
  

• Mohammed J. Zaki. Mining non-redundant association rules. Data Mining and Knowledge Discovery: An International Journal, 9(3):223–248, November 2004.
  [full text] [BibTeX▼]

  @article{2004-nonredundant:dmkd,
      Author = "Zaki, Mohammed J.",
      Title = "Mining Non-Redundant Association Rules",
      Journal = "Data Mining and Knowledge Discovery: An International Journal",
      Volume = "9",
      Number = "3",
      Pages = "223-248",
      Month = "November",
      Year = "2004",
      KeyWord = "Journal,ItemsetMining"
  }
  

• Mohammed J. Zaki, Vinay Nadimpally, Deb Bardhan, and Chris Bystroff. Predicting protein folding pathways. Bioinformatics, 20(1):i386–i393, August 2004. Supplement on the Proceedings of the 12th International Conference on Intelligent Systems for Molecular Biology.
  [full text] [BibTeX▼]

  @article{2004-pathways:bioinfo,
      Author = "Zaki, Mohammed J. and Nadimpally, Vinay and Bardhan, Deb and Bystroff, Chris",
      Title = "Predicting protein folding pathways",
      Journal = "Bioinformatics",
      Note = "Supplement on the Proceedings of the 12th International Conference on Intelligent Systems for Molecular Biology",
      Volume = "20",
      Number = "1",
      Pages = "i386--i393",
      Month = "August",
      Year = "2004",
      Pmid = "15262824",
      KeyWord = "Journal,Proteins"
  }
  

• Mohammed J. Zaki and Limsoon Wong. Data mining techniques. In Limsoon Wong and Louxin Zhang, editors, Selected Topics in Post-Genome Knowledge Discovery, pages 125–163. World Scientific Publishers, 2004.
  [full text] [BibTeX▼]

  @incollection{2004-pgkd,
      Author = "Zaki, Mohammed J. and Wong, Limsoon",
      Editor = "Wong, Limsoon and Zhang, Louxin",
      Title = "Data Mining Techniques",
      Booktitle = "Selected Topics in Post-Genome Knowledge Discovery",
      Pages = "125-163",
      Publisher = "World Scientific Publishers",
      Year = "2004",
      KeyWord = "Chapter,DataMining,Biokdd"
  }
  

• Karlton Sequeira and Mohammed J. Zaki. SCHISM: a new approach for interesting subspace mining. In 4th IEEE International Conference on Data Mining. November 2004.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2004-schism,
      Author = "Sequeira, Karlton and Zaki, Mohammed J.",
      Title = "{SCHISM}: A New Approach for Interesting Subspace Mining",
      Booktitle = "4th IEEE International Conference on Data Mining",
      Month = "November",
      Year = "2004",
      customlinkgithubcode = "https://github.com/zakimjz/SCHISM",
      KeyWord = "Conference,Clustering"
  }
  

• Amir H. Youssefi, David J. Duke, and Mohammed J. Zaki. Visual web mining. In 13th International World Wide Web Conference (Poster). May 2004.
  [full text] [BibTeX▼]

  @inproceedings{2004-vwm,
      Author = "Youssefi, Amir H. and Duke, David J. and Zaki, Mohammed J.",
      Title = "Visual Web Mining",
      Booktitle = "13th International World Wide Web Conference (Poster)",
      Year = "2004",
      Month = "May",
      KeyWord = "Conference,WebMining"
  }
  

2003

• Mohammed J. Zaki, Hannu Toivonen, and Jason Wang, editors. Proceedings of BIOKDD03: ACM SIGKDD International Workshop on Data Mining in Bioinformatics, 2003. URL: http://www.cs.rpi.edu/~zaki/Workshops/BIOKDD03/.
  [BibTeX▼]

  @proceedings{2003-biokdd,
      Author = "Zaki, Mohammed J. and Toivonen, Hannu and Wang, Jason",
      Editor = "Zaki, Mohammed J. and Toivonen, Hannu and Wang, Jason",
      Title = "Proceedings of BIOKDD03: ACM SIGKDD International Workshop on Data Mining in Bioinformatics",
      Year = "2003",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/BIOKDD03/",
      KeyWord = "Book,Biokdd"
  }
  

• Feng Pan, Gao Cong, Anthony K. H. Tung, Jiong Yang, and Mohammed J. Zaki. CARPENTER: finding closed patterns in long biological datasets. In 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2003.
  [full text] [BibTeX▼]

  @inproceedings{2003-carpenter,
      Author = "Pan, Feng and Cong, Gao and Tung, Anthony K. H. and Yang, Jiong and Zaki, Mohammed J.",
      Title = "{CARPENTER}: Finding Closed Patterns in Long Biological Datasets",
      Booktitle = "9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      Month = "August",
      Year = "2003",
      KeyWord = "Conference,GeneExpression,ItemsetMining"
  }
  

• Mohammed J. Zaki, Shan Jin, and Chris Bystroff. Mining residue contacts in proteins using local structure predictions. IEEE Transactions on Systems, Man and Cybernetics – B, 33(5):789–801, October 2003. Special issue on Bioengineering and Bioinformatics.
  [full text] [BibTeX▼]

  @article{2003-contactmining:tsmc,
      Author = "Zaki, Mohammed J. and Jin, Shan and Bystroff, Chris",
      Title = "Mining Residue Contacts in Proteins Using Local Structure Predictions",
      Journal = "IEEE Transactions on Systems, Man and Cybernetics -- B",
      Note = "Special issue on Bioengineering and Bioinformatics",
      Volume = "33",
      Number = "5",
      Pages = "789-801",
      Month = "October",
      Year = "2003",
      KeyWord = "Journal,Proteins"
  }
  

• Mohammed J. Zaki and Karam Gouda. Fast vertical mining using Diffsets. In 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2003.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2003-diffsets,
      Author = "Zaki, Mohammed J. and Gouda, Karam",
      Title = "Fast Vertical Mining Using {Diffsets}",
      Booktitle = "9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      Month = "August",
      Year = "2003",
      customlinkgithubcode = "https://github.com/zakimjz/ECLAT",
      KeyWord = "Conference,ItemsetMining"
  }
  

• Mohammed J. Zaki and Charu C. Aggarwal, editors. Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, June 2003. also as RPI Technical Report 03-05. URL: http://www.cs.rpi.edu/~zaki/Workshops/DMKD03/.
  [full text] [BibTeX▼]

  @proceedings{2003-dmkdw,
      Author = "Zaki, Mohammed J. and Aggarwal, Charu C.",
      Editor = "Zaki, Mohammed J. and Aggarwal, Charu C.",
      Title = "Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery",
      Month = "June",
      Year = "2003",
      Note = "also as RPI Technical Report 03-05",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/DMKD03/",
      KeyWord = "Book,DataMining"
  }
  

• Mohammed J. Zaki, Hannu Toivonen, and Jason Wang. Data mining in bioinformatics: report on biokdd'03. SIGKDD Explorations, 5(2):119–120, December 2003.
  [full text] [BibTeX▼]

  @article{2003-explor,
      Author = "Zaki, Mohammed J. and Toivonen, Hannu and Wang, Jason",
      Title = "Data Mining in Bioinformatics: Report on BIOKDD'03",
      Journal = "SIGKDD Explorations",
      Volume = "5",
      Number = "2",
      Month = "December",
      Pages = "119-120",
      Year = "2003",
      KeyWord = "Journal,Biokdd"
  }
  

• Bouchra Bouqata, Chris Carothers, Boleslaw K. Szymanski, and Mohammed J. Zaki. Understading filesystem performance for data mining applications. In 6th International Workshop on High Performance Data Mining: Pervasive and Data Stream Mining (with SDM). August 2003.
  [full text] [BibTeX▼]

  @inproceedings{2003-filesys,
      Author = "Bouqata, Bouchra and Carothers, Chris and Szymanski, Boleslaw K. and Zaki, Mohammed J.",
      Title = "Understading Filesystem Performance for Data Mining Applications",
      Booktitle = "6th International Workshop on High Performance Data Mining: Pervasive and Data Stream Mining (with SDM)",
      Month = "August",
      Year = "2003",
      KeyWord = "Conference,DataMining"
  }
  

• Bart Goethals and Mohammed J. Zaki, editors. Proceedings of the 1st IEEE ICDM Workshop on Frequent Itemset Mining Implementations, CEUR Workshop Proceedings, 2003. Also as RPI Technical Report 03-04. URL: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-90/.
  [full text] [BibTeX▼]

  @proceedings{2003-fimi,
      Author = "Goethals, Bart and Zaki, Mohammed J.",
      Editor = "Goethals, Bart and Zaki, Mohammed J.",
      Title = "Proceedings of the 1st IEEE ICDM Workshop on Frequent Itemset Mining Implementations",
      Year = "2003",
      Publisher = "CEUR Workshop Proceedings",
      url = "http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-90/",
      Note = "Also as RPI Technical Report 03-04",
      KeyWord = "Book,ItemsetMining"
  }
  

• Vinay Nadimpally and Mohammed J. Zaki. A novel approach to determine normal variation in gene expression data. SIGKDD Explorations, 5(2):4–13, December 2003. Special issue on Microarray Data Analysis.
  [full text] [BibTeX▼]

  @article{2003-geneexpression,
      Author = "Nadimpally, Vinay and Zaki, Mohammed J.",
      Title = "A Novel Approach to Determine Normal Variation in Gene Expression Data",
      Journal = "SIGKDD Explorations",
      Note = "Special issue on Microarray Data Analysis",
      Volume = "5",
      Number = "2",
      Pages = "4-13",
      Month = "December",
      Year = "2003",
      KeyWord = "Journal,GeneExpression"
  }
  

• Mohammed J. Zaki. Mining data in bioinformatics. In Nong Ye, editor, Handbook of Data Mining, pages 573–596. Lawrence Earlbaum Associates, 2003.
  [full text] [BibTeX▼]

  @incollection{2003-hdm,
      Author = "Zaki, Mohammed J.",
      Editor = "Ye, Nong",
      Title = "Mining Data in Bioinformatics",
      Booktitle = "Handbook of Data Mining",
      Pages = "573-596",
      Publisher = "Lawrence Earlbaum Associates",
      Year = "2003",
      KeyWord = "Chapter,Biokdd"
  }
  

• Adriano Veloso, Jr. Wagner Meira, Marcio Carvalho, Srinivasan Parthasarathy, and Mohammed J. Zaki. Incremental and interactive mining for frequent itemsets in evolving databases. In 6th International Workshop on High Performance Data Mining: Pervasive and Data Stream Mining. May 2003.
  [full text] [BibTeX▼]

  @inproceedings{2003-hpdm,
      Author = "Veloso, Adriano and Wagner Meira, Jr. and Carvalho, Marcio and Parthasarathy, Srinivasan and Zaki, Mohammed J.",
      Title = "Incremental and Interactive Mining for Frequent Itemsets in Evolving Databases",
      Booktitle = "6th International Workshop on High Performance Data Mining: Pervasive and Data Stream Mining",
      Month = "May",
      Year = "2003",
      KeyWord = "Conference,ItemsetMining,OnlineDM"
  }
  

• Mohammed J. Zaki and Jason T.L. Wang. Editorial: data management in bioinformatics. Information Systems: An International Journal, 28(4):241–242, June 2003.
  [full text] [BibTeX▼]

  @article{2003-is,
      Author = "Zaki, Mohammed J. and Wang, Jason T.L.",
      Title = "Editorial: Data Management in Bioinformatics",
      Journal = "Information Systems: An International Journal",
      Volume = "28",
      Number = "4",
      Pages = "241-242",
      Month = "June",
      Year = "2003",
      KeyWord = "Journal,GuestEditor,Biokdd"
  }
  

• Karlton Sequeira, Mohammed J. Zaki, and Boleslaw Szymanski. Improving spatial locality using data mining. In 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2003.
  [full text] [BibTeX▼]

  @inproceedings{2003-locality,
      Author = "Sequeira, Karlton and Zaki, Mohammed J. and Szymanski, Boleslaw",
      Title = "Improving Spatial Locality using Data Mining",
      Booktitle = "9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      Month = "August",
      Year = "2003",
      KeyWord = "Conference,DataMining,Systems"
  }
  

• Mohammed J. Zaki and Benjarath Phoophakdee. MIRAGE: a framework for mining, exploring and visualizing minimal association rules. Technical Report 03-4, Computer Science Department, Rensselaer Polytechnic Institute, July 2003.
  [full text] [BibTeX▼]

  @techreport{2003-mirage,
      Author = "Zaki, Mohammed J. and Phoophakdee, Benjarath",
      Title = "{MIRAGE}: A Framework for Mining, Exploring and Visualizing Minimal Association Rules",
      Institution = "Computer Science Department, Rensselaer Polytechnic Institute",
      Number = "03-4",
      Month = "July",
      Year = "2003",
      KeyWord = "TechReport,ItemsetMining"
  }
  

• Ganesh Ramesh, William A. Maniatty, and Mohammed J. Zaki. Feasible itemset distributions in data mining: theory and application. In 22nd ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. June 2003.
  [full text] [BibTeX▼]

  @inproceedings{2003-pods,
      Author = "Ramesh, Ganesh and Maniatty, William A. and Zaki, Mohammed J.",
      Title = "Feasible Itemset Distributions in Data Mining: Theory and Application",
      Booktitle = "22nd {ACM SIGACT-SIGMOD-SIGART} Symposium on Principles of Database Systems",
      Month = "June",
      Year = "2003",
      KeyWord = "Conference,ItemsetMining"
  }
  

• Amir H. Youssefi, David J. Duke, Ephraim P. Glinert, and Mohammed J. Zaki. Towards visual web mining. In 3rd International Workshop on Visual Data Mining (with ICDM). November 2003.
  [full text] [BibTeX▼]

  @inproceedings{2003-vwm,
      Author = "Youssefi, Amir H. and Duke, David J. and Ephraim P. Glinert and Zaki, Mohammed J.",
      Title = "Towards Visual Web Mining",
      Booktitle = "3rd International Workshop on Visual Data Mining (with ICDM)",
      Month = "November",
      Year = "2003",
      KeyWord = "Conference,WebMining"
  }
  

• Mohammed J. Zaki and Charu C. Aggarwal. Xrules: an effective structural classifier for xml data. In 9th ACM SIGKDD International Conference Knowledge Discovery and Data Mining. August 2003.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2003-xrules,
      Author = "Zaki, Mohammed J. and Aggarwal, Charu C.",
      Title = "Xrules: An effective structural classifier for XML data",
      Booktitle = "9th ACM SIGKDD International Conference Knowledge Discovery and Data Mining",
      Month = "August",
      Year = "2003",
      customlinkgithubcode = "https://github.com/zakimjz/Xrules",
      KeyWord = "Conference,TreeMining,Classification"
  }
  

2002

• Karlton Sequeira and Mohammed J. Zaki. Admit: anomaly-base data mining for intrusions. In 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. July 2002.
  [full text] [BibTeX▼]

  @inproceedings{2002-admit,
      Author = "Sequeira, Karlton and Zaki, Mohammed J.",
      Title = "ADMIT: Anomaly-base Data Mining for Intrusions",
      Booktitle = "8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      Month = "July",
      Year = "2002",
      KeyWord = "Conference,Intrusion,Clustering"
  }
  

• Mohammed J. Zaki, Hannu Toivonen, and Jason Wang, editors. Proceedings of BIOKDD02: ACM SIGKDD International Workshop on Data Mining in Bioinformatics, 2002. URL: http://www.cs.rpi.edu/~zaki/Workshops/BIOKDD02/.
  [BibTeX▼]

  @proceedings{2002-biokdd,
      Author = "Zaki, Mohammed J. and Toivonen, Hannu and Wang, Jason",
      Editor = "Zaki, Mohammed J. and Toivonen, Hannu and Wang, Jason",
      Title = "Proceedings of BIOKDD02: ACM SIGKDD International Workshop on Data Mining in Bioinformatics",
      Year = "2002",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/BIOKDD02/",
      KeyWord = "Book,Biokdd"
  }
  

• Mohammed J. Zaki and Ching-Jui Hsiao. CHARM: an efficient algorithm for closed itemset mining. In 2nd SIAM International Conference on Data Mining. April 2002.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2002-charm,
      Author = "Zaki, Mohammed J. and Hsiao, Ching-Jui",
      Title = "{CHARM}: An Efficient Algorithm for Closed Itemset Mining",
      Booktitle = "2nd {SIAM} International Conference on Data Mining",
      Month = "April",
      Year = "2002",
      customlinkgithubcode = "https://github.com/zakimjz/CHARM",
      KeyWord = "Conference,ItemsetMining"
  }
  

• Scott Epter, Mukkai Krishnamoorthy, and Mohammed J. Zaki. Clusterability detection and cluster initialization. In SIAM Workshop on Clustering High Dimensional Data and its Applications (with SDM). April 2002.
  [full text] [BibTeX▼]

  @inproceedings{2002-clusterability,
      Author = "Epter, Scott and Krishnamoorthy, Mukkai and Zaki, Mohammed J.",
      Title = "Clusterability Detection and Cluster Initialization",
      Booktitle = "{SIAM} Workshop on Clustering High Dimensional Data and its Applications (with SDM)",
      Month = "April",
      Year = "2002",
      KeyWord = "Conference,Clustering"
  }
  

• Jingjing Hu, Xiaolan Shen, Yu Shao, Chris Bystroff, and Mohammed J. Zaki. Mining protein contact maps. In 2nd BIOKDD Workshop on Data Mining in Bioinformatics (with SIGKDD). July 2002.
  [full text] [BibTeX▼]

  @inproceedings{2002-contactmap,
      Author = "Hu, Jingjing and Shen, Xiaolan and Shao, Yu and Bystroff, Chris and Zaki, Mohammed J.",
      Title = "Mining Protein Contact Maps",
      Booktitle = "2nd BIOKDD Workshop on Data Mining in Bioinformatics (with SIGKDD)",
      Month = "July",
      Year = "2002",
      KeyWord = "Conference,Proteins,Biokdd"
  }
  

• Ganesh Ramesh, William A. Maniatty, and Mohammed J. Zaki. Indexing and data access methods for database mining. In 7th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. May 2002.
  [full text] [BibTeX▼]

  @inproceedings{2002-dmindexing,
      Author = "Ramesh, Ganesh and Maniatty, William A. and Zaki, Mohammed J.",
      Title = "Indexing and Data Access Methods for Database Mining",
      Booktitle = "7th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery",
      Month = "May",
      Year = "2002",
      KeyWord = "Conference,DataMining,Indexing"
  }
  

• Mohammed J. Zaki and Yi Pan. Introduction: recent developments in parallel and distributed data mining. Distributed and Parallel Databases: An International Journal, 11(2):123–137, March 2002. doi:10.1023/A:1013918601668.
  [full text] [BibTeX▼]

  @article{2002-dpd,
      Author = "Zaki, Mohammed J. and Pan, Yi",
      Title = "Introduction: Recent Developments in Parallel and Distributed Data Mining",
      Journal = "Distributed and Parallel Databases: An International Journal",
      Volume = "11",
      Number = "2",
      Pages = "123-137",
      Month = "March",
      Year = "2002",
      Doi = "10.1023/A:1013918601668",
      KeyWord = "Journal,GuestEditor,ParallelDM"
  }
  

• Adriano Veloso, Bruno Gusmao, Jr. Wagner Meira, Marcio Carvalho, Srinivasan Parthasarathy, and Mohammed J. Zaki. Efficiently mining approximate models of associations in evolving databases. In 6th European Conference on Principles of Knowledge Discovery in Databases. August 2002.
  [full text] [BibTeX▼]

  @inproceedings{2002-evolving-pkdd,
      Author = "Veloso, Adriano and Gusmao, Bruno and Wagner Meira, Jr. and Carvalho, Marcio and Parthasarathy, Srinivasan and Zaki, Mohammed J.",
      Title = "Efficiently Mining Approximate Models of Associations in Evolving Databases",
      Booktitle = "6th European Conference on Principles of Knowledge Discovery in Databases",
      Month = "August",
      Year = "2002",
      KeyWord = "Conference,ItemsetMining,OnlineDM"
  }
  

• Adriano Veloso, Jr. Wagner Meira, Marcio Carvalho, Bruno Possas, Srinivasan Parthasarathy, and Mohammed J. Zaki. Mining frequent itemsets in evolving databases. In 2nd SIAM International Conference on Data Mining. April 2002.
  [full text] [BibTeX▼]

  @inproceedings{2002-evolving-sdm,
      Author = "Veloso, Adriano and Wagner Meira, Jr. and Carvalho, Marcio and Possas, Bruno and Parthasarathy, Srinivasan and Zaki, Mohammed J.",
      Title = "Mining Frequent Itemsets in Evolving Databases",
      Booktitle = "2nd {SIAM} International Conference on Data Mining",
      Month = "April",
      Year = "2002",
      KeyWord = "Conference,ItemsetMining,OnlineDM"
  }
  

• Mohammed J. Zaki, Hannu Toivonen, and Jason Wang. Recent advances in data mining in bioinformatics. SIGKDD Explorations, 4(2):112–114, December 2002.
  [full text] [BibTeX▼]

  @article{2002-explor,
      Author = "Zaki, Mohammed J. and Toivonen, Hannu and Wang, Jason",
      Title = "Recent Advances in Data Mining in Bioinformatics",
      Journal = "SIGKDD Explorations",
      Volume = "4",
      Number = "2",
      Month = "December",
      Pages = "112-114",
      Year = "2002",
      KeyWord = "Journal,Biokdd"
  }
  

• Mohammed J. Zaki. Editorial: online, interactive and anytime data mining. SIGKDD Explorations, 3(2):i–ii, January 2002. URL: http://www.sigkdd.org/explorations/issue.php?volume=3&issue=2&year=2002&month=01.
  [full text] [BibTeX▼]

  @article{2002-explor:editorial,
      Author = "Zaki, Mohammed J.",
      Title = "Editorial: Online, Interactive and Anytime Data Mining",
      Journal = "SIGKDD Explorations",
      Volume = "3",
      Number = "2",
      Month = "January",
      Pages = "i-ii",
      Year = "2002",
      url = "http://www.sigkdd.org/explorations/issue.php?volume=3\&issue=2\&year=2002\&month=01",
      KeyWord = "Journal,GuestEditor,OnlineDM"
  }
  

• John Punin, Mukkai Krishnamoorthy, and Mohammed J. Zaki. Web usage mining: languages and algorithms. In M. Schwaiger and O. Opitz, editors, Explanatory Data Analysis in Empirical Research, Studies in Classification, Data Analysis, and Knowledge Organization, pages 266–281. Springer-Verlag, 2002.
  [full text] [BibTeX▼]

  @incollection{2002-logml,
      Author = "Punin, John and Krishnamoorthy, Mukkai and Zaki, Mohammed J.",
      Editor = "Schwaiger, M. and Opitz, O.",
      Title = "Web Usage Mining: Languages and Algorithms",
      Booktitle = "Explanatory Data Analysis in Empirical Research",
      Series = "Studies in Classification, Data Analysis, and Knowledge Organization",
      Pages = "266-281",
      Publisher = "Springer-Verlag",
      Year = "2002",
      KeyWord = "Chapter,WebMining"
  }
  

• Yu Shao, Malik Magdon-Ismail, Daniel Freedman, Srinivas Akella, Mohammed J. Zaki, and Chris Bystroff. Compression of protein conformational space. In 6th Annual International Conference on Research in Computational Molecular Biology (Poster). April 2002.
  [full text] [BibTeX▼]

  @inproceedings{2002-recomb,
      Author = "Shao, Yu and Magdon-Ismail, Malik and Freedman, Daniel and Akella, Srinivas and Zaki, Mohammed J. and Bystroff, Chris",
      Title = "Compression of Protein Conformational Space",
      Booktitle = "6th Annual International Conference on Research in Computational Molecular Biology (Poster)",
      Month = "April",
      Year = "2002",
      KeyWord = "Conference,Proteins"
  }
  

• Mohammed J. Zaki. Efficiently mining frequent trees in a forest. In 8th ACM SIGKDD International Conference Knowledge Discovery and Data Mining. July 2002.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2002-treeminer,
      Author = "Zaki, Mohammed J.",
      Title = "Efficiently Mining Frequent Trees in a Forest",
      Booktitle = "8th ACM SIGKDD International Conference Knowledge Discovery and Data Mining",
      Annote = "Errata: Theorem 1, case I a) should be as follows: If P != {} add (y,j) and (y,n\_i) to [Px], where n\_i (=n\_j) is the depth first number for node (x,i). (Note that n\_i is easy to compute. It is simply the number of nodes in prefix P, i.e., length of P, not counting -1s). Also, in example 4, the second last line should be: ...adding elements (4,0) and (4,2) to the class...",
      Month = "July",
      Year = "2002",
      customlinkgithubcode = "https://github.com/zakimjz/TreeMiner",
      KeyWord = "Conference,TreeMining"
  }
  

2001

• Mohammed J. Zaki, Hannu Toivonen, and Jason Wang, editors. Proceedings of BIOKDD01: ACM SIGKDD International Workshop on Data Mining in Bioinformatics, 2001. URL: http://www.cs.rpi.edu/~zaki/Workshops/BIOKDD01/.
  [BibTeX▼]

  @proceedings{2001-biokdd,
      Author = "Zaki, Mohammed J. and Toivonen, Hannu and Wang, Jason",
      Editor = "Zaki, Mohammed J. and Toivonen, Hannu and Wang, Jason",
      Title = "Proceedings of BIOKDD01: ACM SIGKDD International Workshop on Data Mining in Bioinformatics",
      Year = "2001",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/BIOKDD01/",
      KeyWord = "Book,Biokdd"
  }
  

• Mohammed J. Zaki and Chris Bystroff. Mining residue contacts in proteins. In R. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, and R. Namburu, editors, Data Mining for Scientific and Engineering Applications, pages 141–164. Kluwer Academic Publishers, Boston, MA, 2001.
  [full text] [BibTeX▼]

  @incollection{2001-contactmining,
      Author = "Zaki, Mohammed J. and Bystroff, Chris",
      Editor = "Grossman, R. and Kamath, C. and Kegelmeyer, P. and Kumar, V. and Namburu, R.",
      Title = "Mining Residue Contacts in Proteins",
      Booktitle = "Data Mining for Scientific and Engineering Applications",
      Pages = "141-164",
      Publisher = "Kluwer Academic Publishers",
      Address = "Boston, MA",
      Year = "2001",
      KeyWord = "Chapter,Proteins,Biokdd"
  }
  

• Mohammed J. Zaki, Hannu Toivonen, and Jason Wang. Biokdd01: workshop on data mining in bioinformatics. SIGKDD Explorations, 3(2):71–73, February 2001.
  [full text] [BibTeX▼]

  @article{2001-explor,
      Author = "Zaki, Mohammed J. and Toivonen, Hannu and Wang, Jason",
      Title = "BIOKDD01: Workshop on Data Mining in Bioinformatics",
      Journal = "SIGKDD Explorations",
      Volume = "3",
      Number = "2",
      Month = "February",
      Pages = "71-73",
      Year = "2001",
      KeyWord = "Journal,Biokdd"
  }
  

• Karam Gouda and Mohammed J. Zaki. Efficiently mining maximal frequent itemsets. In 1st IEEE International Conference on Data Mining. November 2001.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2001-genmax,
      Author = "Gouda, Karam and Zaki, Mohammed J.",
      Title = "Efficiently Mining Maximal Frequent Itemsets",
      Booktitle = "1st IEEE International Conference on Data Mining",
      Month = "November",
      Year = "2001",
      customlinkgithubcode = "https://github.com/zakimjz/Genmax",
      KeyWord = "Conference,ItemsetMining"
  }
  

• John Punin, Mukkai Krishnamoorthy, and Mohammed J. Zaki. LOGML: log markup language for web usage mining. In ACM WEBKDD Workshop on Mining Log Data Across All Customer TouchPoints (with SIGKDD). August 2001.
  [full text] [BibTeX▼]

  @inproceedings{2001-logml,
      Author = "Punin, John and Krishnamoorthy, Mukkai and Zaki, Mohammed J.",
      Booktitle = "ACM WEBKDD Workshop on Mining Log Data Across All Customer TouchPoints (with SIGKDD)",
      Title = "{LOGML}: Log markup language for web usage mining",
      Month = "August",
      Year = "2001",
      KeyWord = "Conference,WebMining"
  }
  

• John Punin, Mukkai Krishnamoorthy, and Mohammed J. Zaki. LOGML: xml language for web usage mining. In 10th International World Wide Web Conference (Poster). May 2001.
  [full text] [BibTeX▼]

  @inproceedings{2001-logml:www,
      Author = "Punin, John and Krishnamoorthy, Mukkai and Zaki, Mohammed J.",
      Title = "{LOGML}: XML language for web usage mining",
      Booktitle = "10th International World Wide Web Conference (Poster)",
      Month = "May",
      Year = "2001",
      KeyWord = "Conference,WebMining"
  }
  

• Srinivasan Parthasarathy, Mohammed J. Zaki, Mitsunori Ogihara, and Wei Li. Parallel data mining for association rules on shared-memory systems. Knowledge and Information Systems, 3(1):1–29, February 2001. doi:10.1007/PL00011656.
  [full text] [BibTeX▼]

  @article{2001-pareclat:kais,
      Author = "Parthasarathy, Srinivasan and Zaki, Mohammed J. and Ogihara, Mitsunori and Li, Wei",
      Title = "Parallel Data Mining for Association Rules on Shared-memory Systems",
      Journal = "Knowledge and Information Systems",
      Volume = "3",
      Number = "1",
      Pages = "1-29",
      Month = "February",
      Year = "2001",
      Doi = "10.1007/PL00011656",
      KeyWord = "Journal,ItemsetMining,ParallelDM"
  }
  

• Mohammed J. Zaki, Vipin Kumar, and David Skillicorn, editors. Proceedings of Workshop on Parallel and Distributed Data Mining, 2001. URL: http://www.cs.rpi.edu/~zaki/Workshops/PDDM01.
  [full text] [BibTeX▼]

  @proceedings{2001-pddm,
      Author = "Zaki, Mohammed J. and Kumar, Vipin and Skillicorn, David",
      Editor = "Zaki, Mohammed J. and Kumar, Vipin and Skillicorn, David",
      Title = "Proceedings of Workshop on Parallel and Distributed Data Mining",
      Year = "2001",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/PDDM01",
      KeyWord = "Book,ParallelDM"
  }
  

• Mohammed J. Zaki, Neal Lesh, and Mitsunori Ogihara. Predicting failures in event sequences. In R. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, and R. Namburu, editors, Data Mining for Scientific and Engineering Applications, pages 141–164. Kluwer Academic Publishers, Boston, MA, 2001.
  [full text] [BibTeX▼]

  @incollection{2001-planmine,
      Author = "Zaki, Mohammed J. and Lesh, Neal and Ogihara, Mitsunori",
      Editor = "Grossman, R. and Kamath, C. and Kegelmeyer, P. and Kumar, V. and Namburu, R.",
      Title = "Predicting Failures in Event Sequences",
      Booktitle = "Data Mining for Scientific and Engineering Applications",
      Pages = "141-164",
      Publisher = "Kluwer Academic Publishers",
      Address = "Boston, MA",
      Year = "2001",
      KeyWord = "Chapter,SequenceMining"
  }
  

• Mohammed J. Zaki. Parallel sequence mining on shared-memory machines. Journal of Parallel and Distributed Computing, 61(3):401–426, March 2001. Special issue on High Performance Data Mining.
  [full text] [BibTeX▼]

  @article{2001-pspade:jpdc,
      Author = "Zaki, Mohammed J.",
      Title = "Parallel Sequence Mining on Shared-Memory Machines",
      Journal = "Journal of Parallel and Distributed Computing",
      Note = "Special issue on High Performance Data Mining",
      Volume = "61",
      Number = "3",
      Pages = "401-426",
      Month = "March",
      Year = "2001",
      KeyWord = "Journal,SequenceMining,ParallelDM"
  }
  

• Mohammed J. Zaki. SPADE: an efficient algorithm for mining frequent sequences. Machine Learning Journal, 42(1/2):31–60, Jan/Feb 2001. Special issue on Unsupervised Learning. doi:10.1023/A:1007652502315.
  [full text] [githubcode] [BibTeX▼]

  @article{2001-spade:mlj,
      Author = "Zaki, Mohammed J.",
      Title = "{SPADE}: An Efficient Algorithm for Mining Frequent Sequences",
      Journal = "Machine Learning Journal",
      Note = "Special issue on Unsupervised Learning",
      Volume = "42",
      Number = "1/2",
      Month = "Jan/Feb",
      Pages = "31-60",
      Year = "2001",
      Doi = "10.1023/A:1007652502315",
      customlinkgithubcode = "https://github.com/zakimjz/SPADE",
      KeyWord = "Journal,SequenceMining"
  }
  

2000

• Krishna Rajan and Mohammed J. Zaki. Data mining through information association: a knowledge discovery tool for materials science. In 17th International CODATA Conference. October 2000.
  [full text] [BibTeX▼]

  @inproceedings{2000-codata,
      Author = "Rajan, Krishna and Zaki, Mohammed J.",
      Title = "Data Mining through Information Association: A Knowledge Discovery Tool for Materials Science",
      Booktitle = "17th International CODATA Conference",
      Month = "October",
      Year = "2000",
      KeyWord = "Conference,Materials"
  }
  

• Mohammed J. Zaki, Shan Jin, and Chris Bystroff. Mining residue contacts in proteins using local structure predictions. In IEEE International Symposium on Bioinformatics and Biomedical Engineering. November 2000. *Best Papers Selection*.
  [full text] [BibTeX▼]

  @inproceedings{2000-contactmining,
      Author = "Zaki, Mohammed J. and Jin, Shan and Bystroff, Chris",
      Title = "Mining Residue Contacts in Proteins Using Local Structure Predictions",
      Booktitle = "IEEE International Symposium on Bioinformatics and Biomedical Engineering",
      Month = "November",
      Year = "2000",
      Note = "*Best Papers Selection*",
      KeyWord = "Conference,Proteins,Biokdd"
  }
  

• Mohammed J. Zaki. Sequences mining in categorical domains: incorporating constraints. In 9th ACM International Conference on Information and Knowledge Management. November 2000.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{2000-cspade,
      Author = "Zaki, Mohammed J.",
      Title = "Sequences Mining in Categorical Domains: Incorporating Constraints",
      Booktitle = "9th ACM International Conference on Information and Knowledge Management",
      Month = "November",
      Year = "2000",
      customlinkgithubcode = "https://github.com/zakimjz/cSPADE",
      KeyWord = "Conference,SequenceMining"
  }
  

• Mohammed J. Zaki. Sequence mining in categorical domains: algorithms and applications. In Ron Sun and Lee Giles, editors, Sequence Learning: Paradigms, Algorithms, and Applications, volume 1828 of LNAI State-of-the-Art-Survey, pages 162–187. Springer-Verlag, Heidelberg, Germany, 2000.
  [full text] [githubcode] [BibTeX▼]

  @incollection{2000-cspade:seqbook,
      Author = "Zaki, Mohammed J.",
      Editor = "Sun, Ron and Giles, Lee",
      Title = "Sequence Mining in Categorical Domains: Algorithms and Applications",
      Booktitle = "Sequence Learning: Paradigms, Algorithms, and Applications",
      Series = "LNAI State-of-the-Art-Survey",
      Volume = "1828",
      Pages = "162-187",
      Publisher = "Springer-Verlag, Heidelberg, Germany",
      Year = "2000",
      customlinkgithubcode = "https://github.com/zakimjz/cSPADE",
      KeyWord = "Chapter,SequenceMining"
  }
  

• Mohammed J. Zaki. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3):372–390, May/Jun 2000. doi:10.1109/69.846291.
  [full text] [githubcode] [BibTeX▼]

  @article{2000-eclat:tkde,
      Author = "Zaki, Mohammed J.",
      Title = "Scalable Algorithms for Association Mining",
      Journal = "IEEE Transactions on Knowledge and Data Engineering",
      Volume = "12",
      Number = "3",
      Pages = "372-390",
      Month = "May/Jun",
      Year = "2000",
      Doi = "10.1109/69.846291",
      customlinkgithubcode = "https://github.com/zakimjz/ECLAT",
      KeyWord = "Journal,ItemsetMining"
  }
  

• Mohammed J. Zaki and Ching-Tien Ho. Workshop report: large-scale parallel kdd systems. SIGKDD Explorations, 1(2):112–114, January 2000.
  [full text] [BibTeX▼]

  @article{2000-explor,
      Author = "Zaki, Mohammed J. and Ho, Ching-Tien",
      Title = "Workshop Report: Large-Scale Parallel KDD Systems",
      Journal = "SIGKDD Explorations",
      Volume = "1",
      Number = "2",
      Month = "January",
      Pages = "112-114",
      Year = "2000",
      KeyWord = "Journal,ParallelDM"
  }
  

• Neal Lesh, Mohammed J. Zaki, and Mitsunori Ogihara. Scalable feature mining for sequential data. IEEE Intelligent Systems and their Applications, 15(2):48–56, Mar/Apr 2000. Special issue on Data Mining.
  [full text] [githubcode] [BibTeX▼]

  @article{2000-featuremine:is,
      Author = "Lesh, Neal and Zaki, Mohammed J. and Ogihara, Mitsunori",
      Title = "Scalable Feature Mining for Sequential Data",
      Journal = "IEEE Intelligent Systems and their Applications",
      Note = "Special issue on Data Mining",
      Volume = "15",
      Number = "2",
      Pages = "48-56",
      Month = "Mar/Apr",
      Year = "2000",
      customlinkgithubcode = "https://github.com/zakimjz/cSPADE",
      KeyWord = "Journal,SequenceMining"
  }
  

• Mohammed J. Zaki, Vipin Kumar, and David Skillicorn, editors. 3rd IPDPS International Workshop on High Performance Data Mining, Springer LINK (http://www.springerlink.com/content/101nv8cq9yrw/), 2000. URL: http://www.cs.rpi.edu/~zaki/Workshops/HPDM00/.
  [full text] [BibTeX▼]

  @proceedings{2000-hpdm,
      Author = "Zaki, Mohammed J. and Kumar, Vipin and Skillicorn, David",
      Editor = "Zaki, Mohammed J. and Kumar, Vipin and Skillicorn, David",
      Title = "3rd IPDPS International Workshop on High Performance Data Mining",
      Publisher = "Springer LINK (http://www.springerlink.com/content/101nv8cq9yrw/)",
      Pages = "348-422",
      Year = "2000",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/HPDM00/",
      KeyWord = "Book,ParallelDM"
  }
  

• Mohammed J. Zaki and Gautam Das, editors. Proceedings of HiPC'00: Special Session on Large Scale Data Mining, Springer LINK (http://www.springerlink.com/content/phpt72qy0ycw), 2000. URL: http://www.cs.rpi.edu/~zaki/SpecialIssues/LSDM/.
  [BibTeX▼]

  @proceedings{2000-lsdm,
      Author = "Zaki, Mohammed J. and Das, Gautam",
      Editor = "Zaki, Mohammed J. and Das, Gautam",
      Title = "Proceedings of HiPC'00: Special Session on Large Scale Data Mining",
      Publisher = "Springer LINK (http://www.springerlink.com/content/phpt72qy0ycw)",
      Pages = "525-566",
      Year = "2000",
      url = "http://www.cs.rpi.edu/\textasciitilde zaki/SpecialIssues/LSDM/",
      KeyWord = "Book,ParallelDM"
  }
  

• Mohammed J. Zaki. Parallel and distributed data mining: an introduction. In Large-Scale Parallel Data Mining, volume 1759 of LNCS/LNAI State-of-the-Art Survey. Springer-Verlag, Heidelberg, Germany, 2000.
  [full text] [BibTeX▼]

  @incollection{2000-lspdm-intro,
      Author = "Zaki, Mohammed J.",
      Title = "Parallel and Distributed Data Mining: An Introduction",
      Booktitle = "Large-Scale Parallel Data Mining",
      Publisher = "Springer-Verlag, Heidelberg, Germany",
      Series = "LNCS/LNAI State-of-the-Art Survey",
      Volume = "1759",
      Year = "2000",
      KeyWord = "Chapter,ParallelDM"
  }
  

• Mohammed J. Zaki. Parallel sequence mining on SMP machines. In Large-Scale Parallel Data Mining, volume 1759 of LNCS/LNAI State-of-the-Art Survey. Springer-Verlag, Heidelberg, Germany, 2000.
  [full text] [BibTeX▼]

  @incollection{2000-lspdm-pspade,
      Author = "Zaki, Mohammed J.",
      Title = "Parallel sequence mining on {SMP} machines",
      Booktitle = "Large-Scale Parallel Data Mining",
      Publisher = "Springer-Verlag, Heidelberg, Germany",
      Series = "LNCS/LNAI State-of-the-Art Survey",
      Volume = "1759",
      Year = "2000",
      KeyWord = "Chapter,ParallelDM,SequenceMining"
  }
  

• Mohammed J. Zaki and Ching-Tien Ho. Large-Scale Parallel Data Mining. Volume 1759 of LNCS/LNAI State-of-the-Art Survey (http://www.springerlink.com/content/dhadtlv2w1yh/). Springer-Verlag, Heidelberg, Germany, 2000. ISBN: 978-3-540-67194-7. URL: http://www.cs.rpi.edu/~zaki/Books/lspdmbook.html.
  [BibTeX▼]

  @book{2000-lspdmbook,
      Author = "Zaki, Mohammed J. and Ho, Ching-Tien",
      Booktitle = "Large-Scale Parallel Data Mining",
      Title = "Large-Scale Parallel Data Mining",
      Publisher = "Springer-Verlag, Heidelberg, Germany",
      Series = "LNCS/LNAI State-of-the-Art Survey (http://www.springerlink.com/content/dhadtlv2w1yh/)",
      Volume = "1759",
      Year = "2000",
      Note = "ISBN: 978-3-540-67194-7",
      Url = "http://www.cs.rpi.edu/\textasciitilde zaki/Books/lspdmbook.html",
      KeyWord = "Book,ParallelDM"
  }
  

• Mohammed J. Zaki. Generating non-redundant association rules. In 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 2000.
  [full text] [BibTeX▼]

  @inproceedings{2000-nonredundant,
      Author = "Zaki, Mohammed J.",
      Title = "Generating Non-Redundant Association Rules",
      Booktitle = "6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
      Month = "August",
      Year = "2000",
      KeyWord = "Conference,ItemsetMining"
  }
  

• William A. Maniatty and Mohammed J. Zaki. A requirement analysis for parallel kdd systems. In 3rd IPDPS Workshop on High Performance Data Mining. May 2000.
  [full text] [BibTeX▼]

  @inproceedings{2000-parKDD,
      Author = "Maniatty, William A. and Zaki, Mohammed J.",
      Title = "A Requirement Analysis for Parallel KDD Systems",
      Booktitle = "3rd IPDPS Workshop on High Performance Data Mining",
      Month = "May",
      Year = "2000",
      KeyWord = "Conference,ParallelDM"
  }
  

• Mohammed J. Zaki, Ching-Tien Ho, and Rakesh Agrawal. Parallel classification on shared-memory systems. In Hillol Kargupta and Philip Chan, editors, Advances in Distributed and Parallel Knowledge Discovery, chapter 14, pages 377–407. AAAI Press, Menlo Park, CA, 2000.
  [full text] [BibTeX▼]

  @incollection{2000-parclassification,
      Author = "Zaki, Mohammed J. and Ho, Ching-Tien and Agrawal, Rakesh",
      Editor = "Kargupta, Hillol and Chan, Philip",
      Title = "Parallel Classification on Shared-Memory Systems",
      Booktitle = "Advances in Distributed and Parallel Knowledge Discovery",
      Chapter = "14",
      Pages = "377-407",
      Publisher = "AAAI Press, Menlo Park, CA",
      Year = "2000",
      KeyWord = "Chapter,ParallelDM,Classification"
  }
  

• Mohammed J. Zaki. Hierarchical parallel algorithms for association mining. In Hillol Kargupta and Philip Chan, editors, Advances in Distributed and Parallel Knowledge Discovery, chapter 13, pages 339–376. AAAI Press, Menlo Park, CA, 2000.
  [full text] [BibTeX▼]

  @incollection{2000-pareclat,
      Author = "Zaki, Mohammed J.",
      Editor = "Kargupta, Hillol and Chan, Philip",
      Title = "Hierarchical Parallel Algorithms for Association Mining",
      Booktitle = "Advances in Distributed and Parallel Knowledge Discovery",
      Chapter = "13",
      Pages = "339-376",
      Publisher = "AAAI Press, Menlo Park, CA",
      Year = "2000",
      KeyWord = "Chapter,ParallelDM,ItemsetMining"
  }
  

• Mohammed J. Zaki, Neal Lesh, and Mitsunori Ogihara. PLANMINE: predicting plan failures using sequence mining. Artificial Intelligence Review, 14(6):421–446, December 2000. Special issue on Applications of Data Mining. doi:https://doi.org/10.1023/A:1006612804250.
  [full text] [BibTeX▼]

  @article{2000-planmine:air,
      Author = "Zaki, Mohammed J. and Lesh, Neal and Ogihara, Mitsunori",
      Title = "{PLANMINE}: Predicting Plan Failures using Sequence mining",
      Journal = "Artificial Intelligence Review",
      Note = "Special issue on Applications of Data Mining",
      Volume = "14",
      Number = "6",
      Pages = "421-446",
      Month = "December",
      Year = "2000",
      DOI = "https://doi.org/10.1023/A:1006612804250",
      KeyWord = "Journal,SequenceMining"
  }
  

• William A. Maniatty and Mohammed J. Zaki. Systems support for scalable data mining. SIGKDD Explorations, 2(2):56–65, December 2000.
  [full text] [BibTeX▼]

  @article{2000-scalabledm,
      Author = "Maniatty, William A. and Zaki, Mohammed J.",
      Title = "Systems Support for Scalable Data Mining",
      Journal = "SIGKDD Explorations",
      Volume = "2",
      Number = "2",
      Pages = "56-65",
      Month = "December",
      Year = "2000",
      KeyWord = "Journal,ParallelDM"
  }
  

• Srinivasan Parthasarathy, Mohammed J. Zaki, Mitsunori Ogihara, and Sandhya Dwarkadas. Sequence mining in dynamic and interactive environments. In Witold Abramowicz and Josef Zurada, editors, Knowledge Discovery for Business Information Systems, pages 377–395. Kluwer Academic Publishers, Boston, MA, 2000.
  [full text] [BibTeX▼]

  @incollection{2000-seq-incremental,
      Author = "Parthasarathy, Srinivasan and Zaki, Mohammed J. and Ogihara, Mitsunori and Dwarkadas, Sandhya",
      Editor = "Abramowicz, Witold and Zurada, Josef",
      Title = "Sequence Mining in Dynamic and Interactive Environments",
      Booktitle = "Knowledge Discovery for Business Information Systems",
      Pages = "377-395",
      Publisher = "Kluwer Academic Publishers, Boston, MA",
      Year = "2000",
      KeyWord = "Chapter,SequenceMining,OnlineDM"
  }
  

1999

• Mohammed J. Zaki, Srinivasan Parthasarathy, and Wei Li. Customized dynamic load balancing for cluster computing. In Rajkumar Buyya, editor, High Performance Cluster Computing: Architectures and Systems, volume 1, pages 582–607. Prentice Hall, Upper Saddle River, NJ, 1999.
  [full text] [BibTeX▼]

  @incollection{1999-dlb,
      Author = "Zaki, Mohammed J. and Parthasarathy, Srinivasan and Li, Wei",
      Editor = "Buyya, Rajkumar",
      Title = "Customized Dynamic Load Balancing for Cluster Computing",
      Booktitle = "High Performance Cluster Computing: Architectures and Systems",
      Volume = "1",
      Pages = "582-607",
      Publisher = "Prentice Hall, Upper Saddle River, NJ",
      Year = "1999",
      KeyWord = "Chapter,Systems"
  }
  

• Neal Lesh, Mohammed J. Zaki, and Mitsunori Ogihara. Mining features for sequence classification. In 5th International Conference on Knowledge Discovery and Data Mining (KDD). August 1999.
  [full text] [BibTeX▼]

  @inproceedings{1999-featuremine,
      Author = "Lesh, Neal and Zaki, Mohammed J. and Ogihara, Mitsunori",
      Title = "Mining features for Sequence Classification",
      Booktitle = "5th International Conference on Knowledge Discovery and Data Mining (KDD)",
      Month = "August",
      Year = "1999",
      KeyWord = "Conference,SequenceMining,Classification"
  }
  

• Mohammed J. Zaki, Ching-Tien Ho, and Rakesh Agrawal. Parallel classification for data mining on shared-memory systems. Technical Report RJ10104, IBM, 1999.
  [full text] [BibTeX▼]

  @techreport{1999-ibmtr,
      Author = "Zaki, Mohammed J. and Ho, Ching-Tien and Agrawal, Rakesh",
      Title = "Parallel Classification for Data Mining on Shared-Memory Systems",
      Institution = "IBM",
      Number = "RJ10104",
      Year = "1999",
      KeyWord = "TechReport,ParallelDM,Classification"
  }
  

• Mohammed J. Zaki and Ching-Tien Ho, editors. Proceedings of the ACM SIGKDD Workshop on Large-Scale Parallel KDD Systems, 1999. URL: http://www.cs.rpi.edu/~zaki/Workshops/WKDD99/.
  [full text] [BibTeX▼]

  @proceedings{1999-lspdm,
      Author = "Zaki, Mohammed J. and Ho, Ching-Tien",
      Editor = "Zaki, Mohammed J. and Ho, Ching-Tien",
      Title = "Proceedings of the ACM SIGKDD Workshop on Large-Scale Parallel KDD Systems",
      Year = "1999",
      Url = "http://www.cs.rpi.edu/\textasciitilde zaki/Workshops/WKDD99/",
      KeyWord = "Book,ParallelDM"
  }
  

• Mohammed J. Zaki, Ching-Tien Ho, and Rakesh Agrawal. Parallel classification for data mining on shared-memory multiprocessors. In 15th IEEE International Conference on Data Engineering. March 1999. See IBM Technical Report RJ10104 \cite 1999-ibmtr for a more detailed version of this paper.
  [full text] [BibTeX▼]

  @inproceedings{1999-par-classification,
      Author = "Zaki, Mohammed J. and Ho, Ching-Tien and Agrawal, Rakesh",
      Title = "Parallel Classification for Data Mining on Shared-Memory Multiprocessors",
      Booktitle = "15th IEEE International Conference on Data Engineering",
      Month = "March",
      Year = "1999",
      Note = "See IBM Technical Report RJ10104\textasciitilde \cite{1999-ibmtr} for a more detailed version of this paper",
      KeyWord = "Conference,ParallelDM,Classification"
  }
  

• Mohammed J. Zaki. Parallel sequence mining on shared-memory machines. In Workshop on Large-Scale Parallel Data Mining Systems (with KDD). August 1999.
  [full text] [BibTeX▼]

  @inproceedings{1999-parspade,
      Author = "Zaki, Mohammed J.",
      Title = "Parallel Sequence Mining on Shared-Memory Machines",
      Booktitle = "Workshop on Large-Scale Parallel Data Mining Systems (with KDD)",
      Month = "August",
      Year = "1999",
      KeyWord = "Conference,SequenceMining,ParallelDM"
  }
  

• Rakesh Agrawal, Ching-Tien Ho, Leon Pauser, and Mohammed J. Zaki. Parallel data mining on shared-memory multiprocessors. In 9th SIAM Conference on Parallel Processing for Scientific Computing, Minisymposium on High-Performance Data Mining. March 1999.
  [full text] [BibTeX▼]

  @inproceedings{1999-pdm-ppsc,
      Author = "Agrawal, Rakesh and Ho, Ching-Tien and Pauser, Leon and Zaki, Mohammed J.",
      Title = "Parallel Data Mining on Shared-Memory Multiprocessors",
      Booktitle = "9th SIAM Conference on Parallel Processing for Scientific Computing, Minisymposium on High-Performance Data Mining",
      Month = "March",
      Year = "1999",
      KeyWord = "Conference,ParallelDM"
  }
  

• Srinivasan Parthasarathy, Mohammed J. Zaki, Mitsunori Ogihara, and Sandhya Dwarkadas. Incremental and interactive sequence mining. In 8th ACM International Conference Information and Knowledge Management. November 1999.
  [full text] [BibTeX▼]

  @inproceedings{1999-seq-incremental,
      Author = "Parthasarathy, Srinivasan and Zaki, Mohammed J. and Ogihara, Mitsunori and Dwarkadas, Sandhya",
      Title = "Incremental and Interactive Sequence Mining",
      Booktitle = "8th ACM International Conference Information and Knowledge Management",
      Month = "November",
      Year = "1999",
      KeyWord = "Conference,SequenceMining,OnlineDM"
  }
  

• Mohammed J. Zaki. Parallel and distributed association mining: a survey. IEEE Concurrency, 7(4):14–25, December 1999. Special issue on Parallel Mechanisms for Data Mining.
  [full text] [BibTeX▼]

  @article{1999-survey:concur,
      Author = "Zaki, Mohammed J.",
      Title = "Parallel and Distributed Association Mining: A Survey",
      Journal = "IEEE Concurrency",
      Note = "Special issue on Parallel Mechanisms for Data Mining",
      Volume = "7",
      Number = "4",
      Pages = "14-25",
      Month = "December",
      Year = "1999",
      KeyWord = "Journal,ItemsetMining,ParallelDM"
  }
  

1998

• Mohammed J. Zaki and Mitsunori Ogihara. Theoretical foundations of association rules. In 3rd ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. June 1998.
  [full text] [BibTeX▼]

  @inproceedings{1998-fca,
      Author = "Zaki, Mohammed J. and Ogihara, Mitsunori",
      Title = "Theoretical Foundations of Association Rules",
      Booktitle = "3rd ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery",
      Month = "June",
      Year = "1998",
      KeyWord = "Conference,ItemsetMining"
  }
  

• Srinivasan Parthasarathy, Mohammed J. Zaki, and Wei Li. Memory placement techniques for parallel association mining. In 4th International Conference on Knowledge Discovery and Data Mining (KDD). August 1998.
  [full text] [BibTeX▼]

  @inproceedings{1998-memplacement,
      Author = "Parthasarathy, Srinivasan and Zaki, Mohammed J. and Li, Wei",
      Title = "Memory placement techniques for parallel association mining",
      Booktitle = "4th International Conference on Knowledge Discovery and Data Mining (KDD)",
      Month = "August",
      Year = "1998",
      KeyWord = "Conference,ItemsetMining,ParallelDM"
  }
  

• Mohammed J. Zaki, Neal Lesh, and Mitsunori Ogihara. PLANMINE: sequence mining for plan failures. In 4th International Conference on Knowledge Discovery and Data Mining (KDD). August 1998.
  [full text] [BibTeX▼]

  @inproceedings{1998-planmine,
      Author = "Zaki, Mohammed J. and Lesh, Neal and Ogihara, Mitsunori",
      Title = "{PLANMINE}: Sequence mining for plan failures",
      Booktitle = "4th International Conference on Knowledge Discovery and Data Mining (KDD)",
      Month = "August",
      Year = "1998",
      KeyWord = "Conference,SequenceMining"
  }
  

• Mohammed J. Zaki, Ching-Tien Ho, and Rakesh Agrawal. Parallel classification on smp systems. In 1st Workshop on High Performance Data Mining (with IPPS). March 1998.
  [full text] [BibTeX▼]

  @inproceedings{1998-smpclassification,
      Author = "Zaki, Mohammed J. and Ho, Ching-Tien and Agrawal, Rakesh",
      Title = "Parallel Classification on SMP Systems",
      Booktitle = "1st Workshop on High Performance Data Mining (with IPPS)",
      Month = "March",
      Year = "1998",
      KeyWord = "Conference,ParallelDM,Classification"
  }
  

• Mohammed J. Zaki. Efficient enumeration of frequent sequences. In 7th ACM International Conference on Information and Knowledge Management. November 1998.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{1998-spade,
      Author = "Zaki, Mohammed J.",
      Title = "Efficient Enumeration of Frequent Sequences",
      Booktitle = "7th ACM International Conference on Information and Knowledge Management",
      Month = "November",
      Year = "1998",
      customlinkgithubcode = "https://github.com/zakimjz/SPADE",
      KeyWord = "Conference,SequenceMining"
  }
  

• Mohammed J. Zaki. Scalable data mining for rules. Technical Report URCS-TR-702 (Ph.D. Thesis), University of Rochester, July 1998.
  [full text] [BibTeX▼]

  @techreport{1998-thesis,
      Author = "Zaki, Mohammed J.",
      Title = "Scalable data mining for rules",
      Institution = "University of Rochester",
      Number = "URCS-TR-702 (Ph.D. Thesis)",
      Month = "July",
      Year = "1998",
      KeyWord = "TechReport,ParallelDM,ItemsetMining,SequenceMining,Classification"
  }
  

1997

• Mohammed J. Zaki, Wei Li, and Srinivasan Parthasarathy. Customized dynamic load balancing for a network of workstations. Journal of Parallel and Distributed Computing, 43(2):156–162, June 1997. Special issue on Workstation Clusters and Network-based Computing: Performance Evaluation, Scheduling, and Fault-Tolerance.
  [full text] [BibTeX▼]

  @article{1997-dlb-jpdc,
      Author = "Zaki, Mohammed J. and Li, Wei and Parthasarathy, Srinivasan",
      Title = "Customized Dynamic Load Balancing for a Network of Workstations",
      Journal = "Journal of Parallel and Distributed Computing",
      Note = "Special issue on Workstation Clusters and Network-based Computing: Performance Evaluation, Scheduling, and Fault-Tolerance",
      Volume = "43",
      Number = "2",
      Pages = "156-162",
      Month = "June",
      Year = "1997",
      KeyWord = "Journal,Systems"
  }
  

• Vineet Gupta, Srinivasan Parthasarathy, and Mohammed J. Zaki. Arithmetic and logic operations with dna. In 3rd DIMACS Workshop on DNA Based Computers. June 1997.
  [full text] [BibTeX▼]

  @inproceedings{1997-dna,
      Author = "Gupta, Vineet and Parthasarathy, Srinivasan and Zaki, Mohammed J.",
      Title = "Arithmetic and Logic Operations with DNA",
      Booktitle = "3rd DIMACS Workshop on DNA Based Computers",
      Month = "June",
      Year = "1997",
      KeyWord = "Conference,DNAComputing"
  }
  

• Mohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, and Wei Li. New algorithms for fast discovery of association rules. In 3rd International Conference on Knowledge Discovery and Data Mining (KDD). August 1997.
  [full text] [githubcode] [BibTeX▼]

  @inproceedings{1997-eclat,
      Author = "Zaki, Mohammed J. and Parthasarathy, Srinivasan and Ogihara, Mitsunori and Li, Wei",
      Title = "New algorithms for fast discovery of association rules",
      Booktitle = "3rd International Conference on Knowledge Discovery and Data Mining (KDD)",
      Month = "August",
      Year = "1997",
      customlinkgithubcode = "https://github.com/zakimjz/ECLAT",
      KeyWord = "Conference,ItemsetMining"
  }
  

• Mohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, and Wei Li. New algorithms for fast discovery of association rules. Technical Report URCS-TR-651, University of Rochester, July 1997.
  [full text] [githubcode] [BibTeX▼]

  @techreport{1997-eclat-tr,
      Author = "Zaki, Mohammed J. and Parthasarathy, Srinivasan and Ogihara, Mitsunori and Li, Wei",
      Title = "New algorithms for fast discovery of association rules",
      Number = "URCS-TR-651",
      Institution = "University of Rochester",
      Month = "July",
      Year = "1997",
      customlinkgithubcode = "https://github.com/zakimjz/ECLAT",
      KeyWord = "TechReport,ItemsetMining"
  }
  

• Michal Cierniak, Mohammed J. Zaki, and Wei Li. Compile-time scheduling algorithms for a heterogeneous network of workstations. The Computer Journal, 40(6):356–372, December 1997. Special issue on Automatic Loop Parallelization. doi:10.1093/comjnl/40.6.356.
  [full text] [BibTeX▼]

  @article{1997-now:cj,
      Author = "Cierniak, Michal and Zaki, Mohammed J. and Li, Wei",
      Title = "Compile-time scheduling algorithms for a heterogeneous network of workstations",
      Journal = "The Computer Journal",
      Note = "Special issue on Automatic Loop Parallelization",
      Volume = "40",
      Number = "6",
      Pages = "356-372",
      Month = "December",
      Year = "1997",
      Doi = "10.1093/comjnl/40.6.356",
      KeyWord = "Journal,Systems"
  }
  

• Mohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara, and Wei Li. Parallel algorithms for discovery of association rules. Data Mining and Knowledge Discovery: An International Journal, 1(4):343–373, December 1997. Special issue on Scalable High-Performance Computing for KDD. doi:10.1023/A:1009773317876.
  [full text] [BibTeX▼]

  @article{1997-pareclat:dmkd,
      Author = "Zaki, Mohammed J. and Parthasarathy, Srinivasan and Ogihara, Mitsunori and Li, Wei",
      Title = "Parallel algorithms for discovery of association rules",
      Journal = "Data Mining and Knowledge Discovery: An International Journal",
      Note = "Special issue on Scalable High-Performance Computing for KDD",
      Volume = "1",
      Number = "4",
      Pages = "343-373",
      Month = "December",
      Year = "1997",
      Doi = "10.1023/A:1009773317876",
      KeyWord = "Journal,ItemsetMining,ParallelDM"
  }
  

• Mohammed J. Zaki, Srinivasan Parthasarathy, Wei Li, and Mitsunori Ogihara. Evaluation of sampling for data mining of association rules. In 7th IEEE International Workshop on Research Issues in Data Engineering (with ICDE). April 1997.
  [full text] [BibTeX▼]

  @inproceedings{1997-sampling-assoc,
      Author = "Zaki, Mohammed J. and Parthasarathy, Srinivasan and Li, Wei and Ogihara, Mitsunori",
      Title = "Evaluation of Sampling for Data Mining of Association Rules",
      Booktitle = "7th IEEE International Workshop on Research Issues in Data Engineering (with ICDE)",
      Month = "April",
      Year = "1997",
      KeyWord = "Conference,ItemsetMining,Sampling"
  }
  

• Mohammed J. Zaki, Srinivasan Parthasarathy, and Wei Li. A localized algorithm for parallel association mining. In 9th Annual ACM Symposium on Parallel Algorithms and Architectures. June 1997.
  [full text] [BibTeX▼]

  @inproceedings{1997-spaa,
      Author = "Zaki, Mohammed J. and Parthasarathy, Srinivasan and Li, Wei",
      Title = "A localized algorithm for parallel association mining",
      Booktitle = "9th Annual ACM Symposium on Parallel Algorithms and Architectures",
      Month = "June",
      Year = "1997",
      KeyWord = "Conference,ParallelDM,ItemsetMining"
  }
  

1996

• Michael Scott, Wei Li, Sandhya Dwarkadas, Leonidas Kontothanassis, Galen Hunt, Maged Michael, Rob Stets, Nikos Hardavellas, Wagner Meira, Alex Poulos, Michal Cierniak, Srinivasan Parthasarathy, and Mohammed J. Zaki. Implementation of cashmere. In 6th International Workshop on Scalable Shared Memory Multiprocessors (with ASPLOS). October 1996.
  [full text] [BibTeX▼]

  @inproceedings{1996-cashmere,
      Author = "Scott, Michael and Li, Wei and Dwarkadas, Sandhya and Kontothanassis, Leonidas and Hunt, Galen and Michael, Maged and Stets, Rob and Hardavellas, Nikos and Meira, Wagner and Poulos, Alex and Cierniak, Michal and Parthasarathy, Srinivasan and Zaki, Mohammed J.",
      Title = "Implementation of Cashmere",
      Booktitle = "6th International Workshop on Scalable Shared Memory Multiprocessors (with ASPLOS)",
      Month = "October",
      Year = "1996",
      KeyWord = "Conference,Systems"
  }
  

• Mohammed J. Zaki, Wei Li, and Srinivasan Parthasarathy. Customized dynamic load balancing for a network of workstations. In 5th IEEE International Symposium on High-Performance Distributed Computing. August 1996.
  [full text] [BibTeX▼]

  @inproceedings{1996-dlb,
      Author = "Zaki, Mohammed J. and Li, Wei and Parthasarathy, Srinivasan",
      Title = "Customized Dynamic Load Balancing for a Network of Workstations",
      Booktitle = "5th IEEE International Symposium on High-Performance Distributed Computing",
      Month = "August",
      Year = "1996",
      KeyWord = "Conference,Systems"
  }
  

• Lane Hemaspaandra, Mohammed J. Zaki, and Marius Zimand. Polynomial-time semi-rankable sets. Journal of Computing and Information, 2(1):50–67, June 1996. Special issue on 8th International Conference of Computing and Information.
  [full text] [BibTeX▼]

  @article{1996-semirankable:jci,
      Author = "Hemaspaandra, Lane and Zaki, Mohammed J. and Zimand, Marius",
      Title = "Polynomial-Time Semi-Rankable Sets",
      Journal = "Journal of Computing and Information",
      Note = "Special issue on 8th International Conference of Computing and Information",
      Volume = "2",
      Number = "1",
      Pages = "50-67",
      Month = "June",
      Year = "1996",
      KeyWord = "Journal,Theory"
  }
  

• Srinivasan Parthasarathy, Wei Li, M. Cierniak, and Mohammed J. Zaki. Compile-time inter-query dependence analysis. In 8th IEEE Symposium on Parallel and Distributed Processing. October 1996.
  [full text] [BibTeX▼]

  @inproceedings{1996-spdp,
      Author = "Parthasarathy, Srinivasan and Li, Wei and Cierniak, M. and Zaki, Mohammed J.",
      Title = "Compile-Time Inter-Query Dependence Analysis",
      Booktitle = "8th IEEE Symposium on Parallel and Distributed Processing",
      Month = "October",
      Year = "1996",
      KeyWord = "Conference,Systems"
  }
  

• Mohammed J. Zaki, Mitsunori Ogihara, Srinivasan Parthasarathy, and Wei Li. Parallel data mining for association rules on shared-memory multi-processors. In Supercomputing'96. November 1996.
  [full text] [BibTeX▼]

  @inproceedings{1996-supercomputing,
      Author = "Zaki, Mohammed J. and Ogihara, Mitsunori and Parthasarathy, Srinivasan and Li, Wei",
      Title = "Parallel Data Mining for Association Rules on Shared-memory Multi-processors",
      Booktitle = "Supercomputing'96",
      Month = "November",
      Year = "1996",
      KeyWord = "Conference,ItemsetMining,ParallelDM"
  }
  

1995

• Mohammed J. Zaki, Wei Li, and Michal Cierniak. Performance impact of processor and memory heterogeneity in a network of machines. In 4th Heterogeneous Computing Workshop (with IPPS). April 1995.
  [full text] [BibTeX▼]

  @inproceedings{1995-hcw,
      Author = "Zaki, Mohammed J. and Li, Wei and Cierniak, Michal",
      Title = "Performance Impact of Processor and Memory Heterogeneity in a Network of Machines",
      Booktitle = "4th Heterogeneous Computing Workshop (with IPPS)",
      Month = "April",
      Year = "1995",
      KeyWord = "Conference,Systems"
  }
  

• Michal Cierniak, Wei Li, and Mohammed J. Zaki. Loop scheduling for heterogeneity. In 4th IEEE International Symposium on High-Performance Distributed Computing. August 1995.
  [full text] [BibTeX▼]

  @inproceedings{1995-hpdc,
      Author = "Cierniak, Michal and Li, Wei and Zaki, Mohammed J.",
      Title = "Loop scheduling for heterogeneity",
      Booktitle = "4th IEEE International Symposium on High-Performance Distributed Computing",
      Month = "August",
      Year = "1995",
      KeyWord = "Conference,Systems"
  }
  

• Olac Fuentes, Jonas Karlsson, Wagner Meira, Rajesh Rao, Terry Riopka, Justinian Rosca, Ramesh Sarukkai, Michael van Wie, Mohammed J. Zaki, Terry Becker, R. Frank, Bradford Miller, and Chris M. Brown. Mobile robotics 1994. Technical Report Technical Report 588, University of Rochester, May 1995.
  [full text] [BibTeX▼]

  @techreport{1995-robotics,
      Author = "Fuentes, Olac and Karlsson, Jonas and Meira, Wagner and Rao, Rajesh and Riopka, Terry and Rosca, Justinian and Sarukkai, Ramesh and van Wie, Michael and Zaki, Mohammed J. and Becker, Terry and Frank, R. and Miller, Bradford and Brown, Chris M.",
      Title = "Mobile Robotics 1994",
      Institution = "University of Rochester",
      Number = "Technical Report 588",
      Month = "May",
      Year = "1995",
      KeyWord = "TechReport,Robotics"
  }