MECA: maximum entropy clustering algorithm

A new approach to fuzzy clustering is presented, which provides the basis for the development of the maximum entropy clustering algorithm (MECA), which is based on an objective function incorporating a measure of the entropy of the membership functions and a measures of the distortion between the prototypes and the feature vectors.

Abstract

This paper presents a new approach to fuzzy clustering, which provides the basis for the development of the maximum entropy clustering algorithm (MECA). The derivation of the proposed algorithm is based on an objective function incorporating a measure of the entropy of the membership functions and a measure of the distortion between the prototypes and the feature vectors. This formulation allows the gradual transition from a maximum uncertainty or minimum selectivity phase to a minimum uncertainty or maximum selectivity phase during the clustering process. Such a transition is achieved by controlling the relative effect of the maximization of the membership entropy and the minimization of the distortion between the prototypes and the feature vectors. The IRIS data set provides the basis for evaluating the proposed algorithms and comparing their performance with that of competing techniques.>