A survey of fuzzy clustering algorithms for pattern recognition - Part I

Clustering algorithms aim at modeling fuzzy (i.e., ambiguous) unlabeled pat terns efficiently. Our goal is to propose a theoretical framework where the expressive power of clustering systems can be compared on the basis of a m eaningful set of common functional features. Part I of this paper reviews t he following issues related to clustering approaches found in the literatur e: relative (probabilistic) and absolute (possibilistic) fuzzy membership f unctions and their relationships to the Bayes rule, batch and on-line learn ing, prototype editing schemes, growing and pruning networks, modular netwo rk architectures, topologically perfect mapping, ecological nets and neuro- fuzziness. From this discussion an equivalence between the concepts of fuzz y clustering and soft competitive learning in clustering algorithms is prop osed as a unifying framework in the comparison of clustering systems. Moreo ver, a set of functional attributes is selected for use as dictionary entri es in the comparison of clustering algorithms, which is the subject of Part II of this paper [1].