Triplet of FCM classifiers

Abstract

This paper proposes the modifications of the fuzzy c-means (FCM) based classifier. The FCM classifier uses covariance structures to represent flexible shapes of clusters. Despite its effectiveness, the intense computation of covariance matrices is an impediment for classifying a set of high-dimensional data. In order to tackle with this problem, we proposed a way of directly handling high-dimensional data in the FCM clustering. The third type of the FCM classifier proposed in this paper is the relational classifier. The classifier treats relational data instead of object data. All these three classifiers are equivalent when the dimensionality of feature vectors is not very large, and the relational data are obtained based on Euclidean distances between object data. Numerical test results reveal the good classification performance of the triplet.