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论文类型： 期刊论文

发表时间： 2011-01-15

发表刊物： PATTERN RECOGNITION LETTERS

收录刊物： Scopus、SCIE、EI

卷号： 32

期号： 2

页面范围： 352-358

ISSN号： 0167-8655

关键字： Clustering; Spectral clustering; Similarity measure

摘要： Similarity measurement is crucial to the performance of spectral clustering The Gaussian kernel function is usually adopted as the similarity measure However with a fixed kernel parameter the similarity between two data points is only determined by their Euclidean distance and is not adaptive to their surroundings In this paper a local density adaptive similarity measure is proposed which uses the local density between two data points to scale the Gaussian kernel function The proposed similarity measure satisfies the clustering assumption and has an effect of amplifying ultra-cluster similarity thus making the affinity matrix clearly block diagonal Experimental results on both synthetic and real world data sets show that the spectral clustering algorithm with our local density adaptive similarity measure outperforms the traditional spectral clustering algorithm the path-based spectral clustering algorithm and the self-tuning spectral clustering algorithm (C) 2010 Elsevier B V All rights reserved