Document Type : Research Paper



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 indices


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