期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2016
卷号：9
期号：10
页码：417-428
出版社：SERSC
摘要：The efficiency of spectral clusteringdepends heavily on the similaritymeasure adopted. A widely used similaritymeasureis the Gaussian kernel function where Euclidean distance is used.Unfortunately,the result of spectral clustering is verysensitive to the scaling parameterand the Euclidean distance is usually not suitable to the complex distribution data.In this paper, a spectral clusteringalgorithmbased on fuzzy partitionsimilarity measure( FPSC)is presentedto solve the problemthatresult of spectral clusteringis very sensitive to scaling parameter. Theproposed algorithm issteady extremely and hardly affected bythe scaling parameter. Experiments on three benchmark datasets, two synthetictexture images are made, and the results demonstrate theeffectiveness of the proposed algorithm.
