Clustering and its validation in a symbolic framework

Abstract

Clustering of symbolic data, using different validity indices, is proposed for determining the optimal number of meaningful clusters. Symbolic objects include linguistic, nominal, boolean, and interval-type of features, along with quantitative attributes. Clustering in this domain involves the use of symbolic dissimilarity between the objects. The novelty of the method lies in transforming the different clustering validity indices, like Normalized Modified Hubert’s statistic, Davies–Bouldin index and Dunn’s index, from the quantitative domain to the symbolic framework. The effectiveness of symbolic clustering is demonstrated on several real life benchmark data sets.