Unsupervised Possibilistic Clustering Based on Kernel Methods

Abstract

Abstract A new kernel based unsupervised clustering algorithm has been proposed. The proposed algorithm is called unsupervised kernel possibilistic clustering algorithm (UKPC), which is an extension of the previously proposed clustering algorithm of unsupervised possibilistic clustering algorithm (UPC). In UKPC, the sample points are mapped into the feature space by the introduced kernel function, and the final clustering partition is obtained by optimizing the objective function of UKPC, which adopts the same clustering rule with UPC clustering model. UKPC has the ability of revealing the non-convex cluster structure because the input data are mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. The contrast experimental results with UPC and other typical fuzzy clustering algorithms show the better performance of the proposed algorithm.