Divisive clustering of symbolic objects using the concepts of both similarity and dissimilarity

A divisive clustering algorithm which makes use of both similarity and dissimilarity is proposed and the results obtained are compared with other methods.

Abstract

A new approach to clustering of symbolic objects which makes use of both similarity and dissimilarity measures is proposed. The proposed modified new similarity and dissimilarity measures will take into consideration the position, span and content of symbolic objects. The similarity and dissimilarity measures used are of new type. The advantages of the proposed modified measures are presented. A divisive clustering algorithm which makes use of both similarity and dissimilarity is proposed. The results obtained by the proposed method is compared with other methods.