A new asymmetric criterion for cluster validation

Abstract

In this paper a new criterion for clusters validation is proposed. Many stability measures to validate a cluster have been proposed such as Normalized Mutual Information. We propose a new criterion for clusters validation. The drawback of the common approach is discussed in this paper and then a new asymmetric criterion is proposed to assess the association between a cluster and a partition which is called Alizadeh-Parvin-Minaei criterion, APM. The APM criterion compensates the drawback of the common Normalized Mutual Information (NMI) measure. Then we employ this criterion to select the more robust clusters in the final ensemble. We also propose a new method named Extended Evidence Accumulation Clustering, EEAC, to construct the matrix of similarity from these selected clusters. Finally, we apply a hierarchical method over the obtained matrix to extract the final partition. The empirical studies show that the proposed method outperforms other ones. © 2011 Springer-Verlag.