A fuzzy clustering model of data and fuzzy c-means

Abstract

The multiple prototype fuzzy clustering model (FCMP), introduced by Nascimento, Mirkin and Moura-Pires (1999), proposes a framework for partitional fuzzy clustering which suggests a model of how the data are generated from a cluster structure to be identified. In the model, it is assumed that the membership of each entity to a cluster expresses a part of the cluster prototype reflected in the entity. In this paper we extend the FCMP framework to a number of clustering criteria, and study the FCMP properties on fitting the underlying proposed model from which data is generated. A comparative study with the fuzzy c-means algorithm is also presented.