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Indexed by:期刊论文

Date of Publication:2011-09-01

Journal:FRONTIERS OF COMPUTER SCIENCE IN CHINA

Included Journals:Scopus、SCIE、EI、CSCD

Volume:5

Issue:3

Page Number:268-278

ISSN No.:1673-7350

Key Words:spectral clustering; random walk; probability transition matrix; matrix perturbation

Abstract:The construction process for a similarity matrix has an important impact on the performance of spectral clustering algorithms. In this paper, we propose a random walk based approach to process the Gaussian kernel similarity matrix. In this method, the pair-wise similarity between two data points is not only related to the two points, but also related to their neighbors. As a result, the new similarity matrix is closer to the ideal matrix which can provide the best clustering result. We give a theoretical analysis of the similarity matrix and apply this similarity matrix to spectral clustering. We also propose a method to handle noisy items which may cause deterioration of clustering performance. Experimental results on real-world data sets show that the proposed spectral clustering algorithm significantly outperforms existing algorithms.