Friday, October 15, 2010
11:00 AM
Computer Studies Bldg. Room 209
Dr. William Cohen
Carnegie Mellon University
Fast Effective Clustering for Graphs and Documents
We describe a new algorithm for clustering nodes in graphs that is simple to implement, easily parallelized, and very fast: on a single machine, it runs in time linear with the number of edges in the graph. Experimentally the method leads to clusterings that are comparable in quality to those produced by widely used spectral methods (e.g., the Normalized Cut algorithm), even though it is asymptotically faster. The method can also be extended to sparse bipartite graphs, such as the graph associated with a text corpora, where nodes can be terms or documents, and edges correspond to term weights in a language model This leads to a very fast document-clustering method. This is joint work with Frank Lin.

Bio: William Cohen received his bachelor's degree in Computer Science from Duke University in 1984, and a PhD in Computer Science from Rutgers University in 1990. From 1990 to 2000 Dr. Cohen worked at AT&T Bell Labs and later AT&T Labs-Research, and from April 2000 to May 2002 Dr. Cohen worked at Whizbang Labs, a company specializing in extracting information from the web. Dr. Cohen is member of the board of the International Machine Learning Society, is an Associate Editor for the journal Artificial Intelligence, and an action editor for the Journal of Machine Learning Research. In the past he has also served as an action editor for the journal Machine Learning and the Journal of Artificial Intelligence Research. He was General Chair for the 2008 International Machine Learning Conference, held July 6-9 at the University of Helsinki, in Finland; Program Co-Chair of the 2006 International Machine Learning Conference; and Co-Chair of the 1994 International Machine Learning Conference. Dr. Cohen was also the co-Chair for the 3rd Int'l AAAI Conference on Weblogs and Social Media, which was held May 17-20, 2009 in San Jose, and is the co-Program Chair for the 4rd Int'l AAAI Conference on Weblogs and Social Media, which will be held May 23-26 at George Washington University in Washington, D. C. He is a AAAI Fellow, and in 2008, he won the SIGMOD "Test of Time" Award for the most influential SIGMOD paper of 1998. Dr. Cohen has also served on more than 20 other program committees or advisory committees.

Dr. Cohen's research interests include information integration and machine learning, particularly information extraction, text categorization and learning from large datasets. He holds seven patents related to learning, discovery, information retrieval, and data integration, and is the author of more than 100 publications.

Refreshments will be provided at 10:45 AM