Publications and Presentations

2008

• Varun Chandola, Arindam Banerjee, and Vipin Kumar, "Anomaly Detection: A Survey," October 2008.
  Abstract: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with. — Download pdf, 613 KB
• DDMC newsletter Winter 2008, Download pdf (366 KB)

2007

• Gediminas Adomavicius and Jesse Bockstedt, "C-TREND: A New Technique for Indentifying and Visualizing Trends in Multi-Attribute Transactional Data," DTC Research Report 2007/45, October 2007 — Download pdf, 653 KB
• Arindam Banerjee and Hanhuai Shan, "Latent Dirichlet Conditional Naive-Bayes Models," IEEE International Conference on Data Mining (ICDM), DTC Research Report 2007/44, September 2007 — Download pdf, 295 KB

2006

• Nitin Karnani and Shashi Shekhar, “Digitizing Tool For Jane Goodall’s Chimpanzee Project” — Download pdf, 724 KB
• Durga Gumaste and Shashi Shekhar, “Design data retrieval and manipulation for subset of ‘Gombe’ database using QBE” — Download pdf, 600 KB
• Arindam Banerjee and Joydeep Ghosh, “Scalable Clustering with Balancing Constraints,” Data Mining and Knowledge Discovery, v. 13, no. 3, November 2006, pg. 365-395 — Download pdf, 435 KB
• Sugato Basu, Mikhail Bilenko, Arindam Banerjee, and Raymond Mooney, “Semi-supervised Clustering with Constraints,” Semi-Supervised Learning, MIT Press, Cambridge, MA, 2006.
• Arindam Banerjee, Chase Krumpelman, Sugato Basu, Raymond J. Mooney, and Joydeep Ghosh, “Model-based Overlapping Clustering,” International Conference on Knowledge Discovery and Data Mining (KDD), 2005 — Download pdf, 182 KB
• Clustering with Bregman Divergences A. Banerjee, S. Merugu, I. Dhillon and J. Ghosh. SIAM International Conference on Data Mining (SDM) (2004) BEST PAPER AWARD http://www.lans.ece.utexas.edu/~abanerjee/papers/05/banerjee05b.pdf (Journal version (JMLR))
• A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, D. Modha. International Conference on Knowledge Discovery and Data Mining (KDD) (2004) http://www.lans.ece.utexas.edu/~abanerjee/papers/04/kdd04coclust.ps
• An Objective Evaluation Crietrion for Clustering A. Banerjee and J. Langford. International Conference on Knowledge Discovery and Data Mining (KDD) (2004) http://www.lans.ece.utexas.edu/~abanerjee/papers/04/pacmdl.ps
• Active Semi-supervision for Pairwise Constrained Clustering S. Basu, A. Banerjee and R. Mooney. SIAM International Conference on Data Mining (SDM) (2004) http://www.cs.utexas.edu/users/ml/papers/semi-sdm-04.pdf
• Generative Model-based Clustering of Directional Data A. Banerjee, I. Dhillon, J. Ghosh and S. Sra. International Conference on Knowledge Discovery and Data Mining
• “Probabilistic Semi-supervised Clustering with Constraints”
  S. Basu, M. Bilenko, A. Banerjee, and R. Mooney,
• “Data Mining Initiative @ Minnesota: A University-Industry Partnership”
  Jaideep Srivastava, Computer Science & Engineering — Download pdf, 3 MB
• “Industrial Collaborations: Data Mining”
  Jim Licari Assistant Director for Industrial Liaison — Download pdf, 2.7 MB
• “Summarization — Compressing Data into an Informative Representation,” Download pdf, 137 KB
• “Generalizing the Notion of Confidence,” Download pdf, 390 KB
• “Data Mining for Customer Relationship Management,” Download pdf, 63 KB
• “Why Data Mining,” Download pdf, 1.1 MB
• Hui Xiong, Gaurav Pandey, Michael Steinbach, and Vipin Kumar, “Enhancing Data Analysis with Noise Removal,” IEEE Transactions on Knowledge and Data Engineering, vol 18, no. 3, March 2006, Download pdf, 721 KB
• Hui Xiong, Shashi Shekhar, Pang-Ning Tan, and Vipin Kumar, “TAPER: A Two-Step Approach for All-Strong-Pairs Correlation Query in Large Databases,” IEEE Transactions on Knowledge and Data Engineering, vol 18, no.4, April 2006, Download pdf, 766 KB