Designing Gaussian Membership Functions for Fuzzy Classifier Generated by Heuristic Possibilistic Clustering

Dmitri Arkady Viattchenin, Reyhane Tati, Aliaksandr Damaratski

Abstract


The paper deals with the problem of constructing Gaussian membership functions of fuzzy sets for fuzzy rules derived from the data by using heuristic algorithms of possibilistic clustering. Basic concepts of the heuristic approach to possibilistic clustering are reminded and the extended technique of constructing membership functions of fuzzy sets is proposed. An illustrative example is given and preliminary conclusions are made.

Keywords


fuzzy cluster, fuzzy rule, antecedent, consequent, Gaussian membership function

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Journal of Information and Organizational Sciences (Online)
ISSN 1846-9418 (online)
ISSN 1846-3312 (print)