Parallel Spectral Clustering

Authors:
Yangqiu Song;Wen-Yen Chen;Hongjie Bai;Chih-Jen Lin;Edward Y. Chang
Affiliations:
Department of Automation, Tsinghua University, Beijing, China and Google Research, , USA/China;Department of Computer Science, University of California, Santa Barbara, USA and Google Research, , USA/China;Google Research, , USA/China;Department of Computer Science, National Taiwan University, Taipie, Taiwan and Google Research, , USA/China;Google Research, , USA/China
Venue:
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Year:
2008

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Abstract

Spectral clustering algorithm has been shown to be more effective in finding clusters than most traditional algorithms. However, spectral clustering suffers from a scalability problem in both memory use and computational time when a dataset size is large. To perform clustering on large datasets, we propose to parallelize both memory use and computation on distributed computers. Through an empirical study on a large document dataset of 193,844 data instances and a large photo dataset of 637,137, we demonstrate that our parallel algorithm can effectively alleviate the scalability problem.