Kernelized fuzzy attribute C-means clustering algorithm

Authors:
Jingwei Liu;Meizhi Xu
Affiliations:
LMIB and Department of Mathematics, Beijing University of Aeronautics and Astronautics, Beijing 100083, PR China;Department of Mathematics, Tsinghua University, Beijing 100084, PR China
Venue:
Fuzzy Sets and Systems
Year:
2008

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Abstract

A novel kernelized fuzzy attribute C-means clustering algorithm is proposed in this paper. Since attribute means clustering algorithm is an extension of fuzzy C-means algorithm with weighting exponent m=2, and fuzzy attribute C-means clustering is a general type of attribute C-means clustering with weighting exponent m1, we modify the distance in fuzzy attribute C-means clustering algorithm with kernel-induced distance, and obtain kernelized fuzzy attribute C-means clustering algorithm. Kernelized fuzzy attribute C-means clustering algorithm is a natural generalization of kernelized fuzzy C-means algorithm with stable function. Experimental results on standard Iris database and tumor/normal gene chip expression data demonstrate that kernelized fuzzy attribute C-means clustering algorithm with Gaussian radial basis kernel function and Cauchy stable function is more effective and robust than fuzzy C-means, fuzzy attribute C-means clustering and kernelized fuzzy C-means as well.