Dr. Pang-Ning Tan is a Professor in the Department of Computer Science and Engineering at MSU. He received his M.S degree in Physics and Ph.D. degree in Computer Science from University of Minnesota. His research interests span a broad range of data mining problems, from pattern discovery (association analysis, anomaly detection, and cluster analysis) to predictive modeling. In addition to addressing fundamental problems in data mining, he is also interested in applying data mining techniques to various application domains including climate and Earth sciences, social and information networks, botnet and webspam detection, and medical informatics. His research has been supported by the National Science Foundation, Office of Naval Research, Army Research Office, National Aeronautics and Space Administration, and Michigan State University.
Ph.D., Computer Science, University of Minnesota 2002
M.S., Physics, University of Minnesota 1996
Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar Addison Wesley, ISBN: 0-321-32136-7
Prakash Mandayam-Comar, Pang-Ning Tan, and Anil Jain. A Framework for Joint Community Detection Across Multiple Related Networks, accepted for Neurocomputing Journal (2011)
Prakash Mandayam-Comar, Pang-Ning Tan, and Anil K Jain, LinkBoost: A Novel Cost-Sensitive Boosting Framework for Community-Level Network Link Prediction, Proceedings of IEEE International Conference on Data Mining (ICDM 2011), Vancouver, Canada, December 11-14 (2011)
Zubin Abraham, Fan Xin, and Pang-Ning Tan Smoothed Quantile Regression for Statistical Downscaling of Extreme Events in Climate Modeling, Proceedings of NASA Conference on Intelligent Data Understanding (CIDU 2011), Mountain View, California, October 19-21 (2011)
Feilong Chen, Supranamaya Ranjan, and Pang-Ning Tan Detecting bots via Incremental LS-SVM Learning with Dynamic Feature Adaptation, Proceedings of ACM SIGKDD International Conference on Data Mining (KDD 2011), San Diego, California, August 21-24 (2011)