A Survey of Fuzzy Clustering Algorithms for Pattern Recognition

Abstract

Clustering algorithms aim at modelling fuzzy (i.e., ambiguous) unlabeled patterns efficiently. Our goal is to propose a theoretical framework where clustering systems can be compared on the basis of their learning strategies. In the first part of this work, the following issues are reviewed: relative (probabilistic) and absolute (possibilistic) fuzzy membership functions and their relationships to the Bayes rule, batch and on-line learning, growing and pruning networks, modular network architectures, topologically perfect mapping, ecological nets and neuro-fuzziness. From this discussion an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed as a unifying framework in the comparison of clustering systems. Moreover, a set of functional attributes is selected for use as dictionary entries in our comparison. In the second part of this paper, five clustering algorithms taken from the literature are reviewed and compared on..