Kernelized fuzzy attribute C-means clustering algorithm

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 m>1, 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.