Analysis and optimization of fuzzy c-means clustering with various entropy maximization methods

Abstract

Fuzzy c-means clustering using entropy maximization method consists of an entropy function, an annealing method, a cooling schedule, and normalization constraint. There exist a few possibilities for each component, for example, Shannon entropy, fuzzy entropy and so on. An optimal combination and an optimal setting of parameters of these components for a given dataset are still unknown. In this article, by changing the combination of the components, we examine and clarify characteristic of each method from this point of view.