A survey of fuzzy clustering algorithms for pattern recognition. I

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 29; no. 6; pp. 778 - 785
• Main Authors: Baraldi, A., Blonda, P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.1999
• Subjects: Algorithms; Biological system modeling; Clustering; Clustering algorithms; Cybernetics; Dictionaries; Fuzzy; Fuzzy logic; Fuzzy neural networks; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Learning; Networks; Pattern recognition; Power system modeling; Prototypes
• ISSN: 1083-4419
• DOI: 10.1109/3477.809032

Clustering algorithms aim at modeling fuzzy (i.e., ambiguous) unlabeled patterns efficiently. Our goal is to propose a theoretical framework where the expressive power of clustering systems can be compared on the basis of a meaningful set of common functional features. Part I of this paper reviews the following issues related to clustering approaches found in the literature: relative (probabilistic) and absolute (possibilistic) fuzzy membership functions and their relationships to the Bayes rule, batch and on-line learning, prototype editing schemes, 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 the comparison of clustering algorithms, which is the subject of part II of this paper.