A Metric to Evaluate a Cluster by Eliminating Effect of Complement Cluster

Abstract

In this paper a new criterion for clusters validation is proposed. This new cluster validation criterion is used to approximate the goodness of a cluster. A clustering ensmble framework based on the new metric is proposed. In the framework first a large number of clusters are prepared and then some of them are selected for final ensmble. The clusters which satisfy a threshold of the proposed metric are selected to participate in final clustering ensemble. For combining the chosen clusters, a co-association based consensus function is applied. Since the Evidence Accumulation Clustering (EAC) method cannot derive the co-association matrix from a subset of clusters, a new EAC based method which is called Extended EAC, EEAC, is applied for constructing the co-association matrix from the subset of clusters. Employing this new cluster validation criterion, the obtained ensemble is evaluated on some well-known and standard data sets. The empirical studies show promising results for the ensemble obtained using the proposed criterion comparing with the ensemble obtained using the standard clusters validation criterion.