A new kernel-based possibilistic intuitionistic fuzzy c-means clustering

Abstract

Fuzzy c-means and its derivatives such as possibilistic c-means and possibilistic fuzzy c-means are the most widely used clustering algorithms in the literature. Though efficient, these clustering algorithms do not achieve high cluster quality on real-world datasets, which are not linearly separable. Kernel-based clustering algorithms employ nonlinear similarity measures to define the inter-point similarities. As a result, they are able to identify clusters of arbitrary shapes and densities. Comparative analysis over standard datasets has established the superiority of kernel methods over its corresponding standard algorithms. In this paper, we propose a kernel-based Atanassov's possibilistic intuitionistic fuzzy clustering for data clustering and image segmentation. The paper explores the performance of the proposed methodology with respect to various internal and external indices for various real datasets and it is found to perform better than other clustering techniques in the sequel, i.e., normal as well as kernel-based algorithms. Experimental results on noisy image datasets also show the competence of the proposed approach.