A Validity Index Based on Connectivity

Abstract

In this paper we have developed a connectivity based cluster validity index. This validity index is able to detect the number of clusters automatically from data sets having well separated clusters of any shape, size or convexity. The proposed cluster validity index, connect-index, uses the concept of relative neighborhood graph for measuring the amount of "connectedness" of a particular cluster. The proposed connect-index is inspired by the popular Dunn's index for measuring the cluster validity. Single linkage clustering algorithm is used as the underlying partitioning technique. The superiority of the proposed validity measure in comparison with Dunn's index is shown for four artificial and two real-life data sets.