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AIC Seminar Series

Overlapping Clusters

Nina MishraUniversity of Virginia[Home Page]

Notice:  hosted by Jeffrey Davitz

Date:  2007-05-16 at 10:00

Location:  EJ228  (Directions)

   Abstract

Clustering is one of the most widely used data mining algorithms with applications as diverse as customer segmentation and community discovery in social networks. This talk considers two clustering problems: finding good cluster descriptions and finding dense graph clusters. A recurring theme of our work is a move away from finding a strict partition of the data by allowing clusters to overlap and by skipping selected outlier data points to achieve more meaningful results. We propose new clustering objectives that intuitively capture these practical needs. For cluster descriptions, we seek a collection of conjunctive descriptions that each cover a large collection of points. For graph clusters, we seek internally dense and externally sparse subgraphs. Simple algorithms will be described to find such clusters.

   Bio for Nina Mishra

Nina Mishra is currently an Associate Professor at the University of Virginia. Prior to that, she held a joint appointment as a Senior Research Scientist at HP Labs and as an Acting Faculty member at Stanford University. Her research interests are in the design and analysis of data mining, machine learning and privacy-preserving algorithms. She serves on the editorial boards for the Machine Learning journal, IEEE TKDE Journal, IEEE Intelligent Systems, and the Journal of Privacy and Confidentiality. She served as Program Chair for the ICML’03 conference (International Conference on Machine Learning) and has served on numerous data mining and machine learning program committees. She received a PhD in Computer Science from the University of Illinois at Urbana-Champaign.

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