Cluster Stability Assessment Based on Theoretic Information Measures

Abstract. Cluster validation to determine the right number of clusters is an important issue in clustering processes. In this work, a strategy to address the problem of cluster validation based on cluster stability properties is introduced. The stability index proposed is based on information measures taking into account the variation on some of these measures due to the variability in clustering solutions produced by different sample sets of the same problem. The experiments carried out on synthetic and real database show the effectiveness of the cluster stability index when the clustering algorithm is based on a data structure model adequate to the problem