On Bezdek-like Fuzzy c-Means based on Maximization

Abstract

In this report, a maximization-based Bezdek-like Fuzzy c-Means clustering methodology is proposed. Standard fuzzy c-means and its many variants are based on minimization model, which contrast with a method based on maximization model derived from entropy-regularization of spherical K-means. This report shows the fuzzification parameter less than 1 yields maximization-based Bezdek-like Fuzzy c-Means clustering method. Following to this manner, a maximization-based fuzzy nonmetric model and a maximization-based Bezdek-like fuzzy co-clustering model are proposed. Furthermore, some kernelized maximization-based Bezdek-like fuzzy c-means clustering algorithms are proposed, two of which have eigenvalue subproblem.