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

Dmitri Arkady Viattchenin; Reyhane Tati; Aliaksandr Damaratski

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

Authors

Dmitri Arkady Viattchenin United Institute of Informatics Problems National Academy of Sciences of Belarus, Minsk, Belarus
Reyhane Tati Department of Computer Islamic Azad University, Doroud Branch, Lorestan, Iran
Aliaksandr Damaratski United Institute of Informatics Problems National Academy of Sciences of Belarus, Minsk, Belarus

Keywords:

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

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.

Downloads

Published

2013-12-10

How to Cite

[1]

D. A. Viattchenin, R. Tati, and A. Damaratski, “Designing Gaussian Membership Functions for Fuzzy Classifier Generated by Heuristic Possibilistic Clustering”, J. inf. organ. sci. (Online), vol. 37, no. 2, Dec. 2013.