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Seminar abstract

Large Scale Clustering using Approximate Kernel K-means

Rong Jin
Associate Professor
Department of Computer Science and Engineering, Michigan State University

Abstract : Digital data explosion in recent times mandates the development of scalable clustering algorithms to organize the data in a meaningful and easily accessible form. Most clustering algorithms proposed in the literature to handle large datasets assume linear separability. Kernel based clustering algorithms, on the other hand, capture the non-linear structure of data and have been found to be more effective on real world datasets. However, kernel based algorithms are not scalable to large data sets because their running-time complexity and memory requirements are quadratic in the number of data instances. In this paper, we propose an approximation scheme for kernel K-means, termed approximate kernel K-means, that reduces both the computational complexity and the memory requirements by employing a randomized approach. We prove analytically and demonstrate empirically that the proposed algorithm's clustering performance is similar to that of the classical kernel K-means algorithm, but with dramatically reduced running time and memory requirements.

Bio: Dr. Jin is an Associative professor of the Computer and Science Engineering Dept. of Michigan State University. His research interest is statistical machine learning and its application to information retrieval. He is currently an associative editor of ACM Transactions on Knowledge Discovery from Data. Dr. Jin received his Ph.D. degree from Carnegie Mellon University in 2003, and is a recipient of NSF Career Award in 2006 .
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