Modified possibilistic clustering model based on kernel methods

Abstract

A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means (KMPCM) model. The proposed model is an extension of the modified possibilistic c-means (MPCM) algorithm by using kernel methods. Different from MPCM and fuzzy c-means (FCM) model which are based on Euclidean distance, the proposed model is based on kernel-induced distance. Furthermore, with kernel methods the input data can be mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. It is unnecessary to do calculation in the high-dimensional feature space because the kernel function can do it. Numerical experiments show that KMPCM outperforms FCM and MPCM.