A new exponential cluster validity index using Jaccard distance

Estimating the optimal number of clusters in an unsupervised partitioning of data sets has been a challenging area in recent years. These indices usually use two criteria called compactness and separation to evaluate the efficiency of the performed clustering. In this paper a new separation measure for ECAS cluster validity index, proposed by Fazel et al. [1] is identified, which uses Jaccard distance in order to consider the whole shape of clusters. Jaccard distance uses the size of intersection and union of fuzzy sets, giving the cluster validity index more information about the overlap and separation of clusters. This property results in high robustness of the proposed index dealing with various degrees of fuzziness in comparison with ECAS. To test the efficiency of the proposed index in comparison with nine other indices existing in the literature, 15 data sets (3 existing datasets and 12 artificial data sets) have been used. Computational results indicate robustness and high capability of the proposed index in comparison with previous indice