NIVA: a Robust cluster validity

Clustering aims at extracting hidden structures in datasets. Many validity indices have been proposed to evaluate clustering results; some of them work well when clusters have different densities and sizes and others with different shapes. They usually have a tendency to consider one or two characteristics simultaneously. In this paper, we present a cluster validity index that takes advantage of the density, size and shape of cluster characteristics. The proposed index is experimentally compared with PS, CS and S_Dbw indices using 12 synthetic datasets. Our proposed index improves others indices.