Fuzzy clustering with ε-hyperballs and its application to data classification

Abstract

In the presented paper the Fuzzy Clustering with ε-Hyperballs being the prototypes is proposed. It is based on the idea of regions of insensitivity - described by the hyperballs of radius ε, in which the distances of objects from the centers of the hyperballs are considered as equal to zero. The proposed clustering was applied to determine the parameters of fuzzy sets in antecedents of the classifier based on fuzzy if-then rules. The classification quality obtained for six benchmark datasets was compared with the reference classifiers. The results show the improvement of the classification accuracy using the proposed method.