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Publications and Presentations
2008
2007
- Varun Chandola, Arindam Banerjee, and Vipin Kumar, "Outlier
Detection — A Survey," August 2007.
Abstract: Outlier detection has been a very
important concept in the realm of data analysis. Recently, several
application domains have realized the direct mapping between outliers
in data and real world anomalies, that are of great interest to an
analyst. Outlier detection has been researched within various application
domains and knowledge disciplines. This survey provides a comprehensive
overview of existing outlier detection techniques by classifying them
along different dimensions. —
Download pdf,
546 KB
- 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
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