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Seminar abstract


Clustering and Co-clustering with Bregman Divergences

Joydeep Ghosh
Schlumberger Distinguished Centennial Chair Professor, IEEE Fellow
Dept. of Electrical & Computer Engineering
The University of Texas at Austin, USA

Abstract :

Bregman divergences form a large class of "distance" or loss functions with certain common properties. In the first part of this talk, I will show how the simple k-means algorithm, which is related to a squared Euclidean loss function, can be generalized to loss functions for all Bregman divergences. Further, we show an explicit bijection between Bregman divergences and exponential families, and use it to derive a simple soft clustering algorithm for all Bregman divergences. Together these two results enable hard/soft clustering of a very wide range of data types with their corresponding noise models. In the second part, we introduce the powerful idea of co-clustering and propose a general framework for performing it under a variety of loss functions and domain constraints. The minimum Bregman information solution, a direct generalization of maximum entropy and least squares principles, plays a critical role in the analysis that leads an elegant meta-algorithm guaranteed to achieve local optimality. Some applications of co-clustering will be illustrated as well.


Joydeep Ghosh is currently the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at the University of Texas, Austin. He joined the UT-Austin faculty in 1988 after being educated at IIT Kanpur, (B. Tech '83) and The University of Southern California (Ph.D’88). He is the founder-director of IDEAL (Intelligent Data Exploration and Analysis Lab) and a Fellow of the IEEE. His research interests lie primarily in intelligent data analysis, data mining and web mining, adaptive multi-learner systems, and their applications to a wide variety of complex engineering and AI problems.

Dr. Ghosh has published more than 250 refereed papers and 35 book chapters, and co-edited 20 books. His research has been supported by the NSF, Yahoo!, Google, ONR, ARO, AFOSR, Intel, IBM, Motorola, TRW, Schlumberger and Dell, among others. He received the 2005 Best Research Paper Award from UT Co-op Society and the 1992 Darlington Award given by the IEEE Circuits and Systems Society for the Best Paper in the areas of CAS/CAD, besides ten other "best paper" awards over the years. He was the Conference Co-Chair of Computational Intelligence and Data Mining (CIDM’07), Program Co-Chair for ICPR'08 (Pattern Recognition Track), The SIAM Int'l Conf. on Data Mining (SDM'06), and Conf. Co-Chair for Artificial Neural Networks in Engineering (ANNIE)'93 to '96 and '99 to '03. He is the founding chair of the Data Mining Tech. Committee of the IEEE CI Society. He also serves on the program committee of several top conferences on data mining, neural networks, pattern recognition, and web analytics every year. Dr. Ghosh has been a plenary/keynote speaker on several occasions such as ISIT'08, ANNIE’06, MCS 2002 and ANNIE'97 and, and has widely lectured on intelligent analysis of large-scale data. He has co-organized workshops on high dimensional clustering (ICDM 2003; SDM 2005), Web Analytics (with SIAM Int'l Conf. on Data Mining, SDM2002), Web Mining (with SDM 2001), and on Parallel and Distributed Knowledge Discovery (with KDD-2000).

Dr. Ghosh has served as a co-founder, consultant or advisor to successful startups (Stadia Marketing, Neonyoyo and Knowledge Discovery One) and as a consultant to large corporations such as IBM, Motorola and Vinson & Elkins. At UT, Dr. Ghosh teaches graduate courses on data mining, artificial neural networks, and web analytics. He was voted the Best Professor by the Software Engineering Executive Education Class of 2004.
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