Fuzzy clustering using fuzzy competitive learning networks

Abstract

The author presents the results of using fuzzy neural network modeling and learning techniques to search for fuzzy clusters of unlabeled patterns. The goal is to embed fuzzy clustering into neural networks so that online learning and parallel implementation are feasible. Fuzzy competitive learning networks are investigated based on the conventional competitive learning networks, and some implications of these results for interpreting fuzziness by the network are discussed. The derivation of such modeling and learning techniques illustrates how the idea of incorporating fuzziness into conventional neural networks might be realized. The necessity of dealing with the fuzzy features in pattern classification requires modifications of neural networks and associated learning methods.<>