Abstract
Many stability measures to validate a cluster have been proposed such as Normalized Mutual Information. 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-Moshki-Minaei criterion, APMM. The APMM criterion compensates the drawback of the common Normalized Mutual Information (NMI) measure. Also, a clustering ensemble method is proposed which is based on aggregating a subset of primary clusters. This method uses the Average APMM as fitness measure to select a number of clusters. The clusters which satisfy a predefined threshold of the mentioned measure are selected to participate in the clustering ensemble. To combine the chosen clusters, a co-association based consensus function is employed. 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 employed to construct the co-association matrix from the chosen subset of clusters. The empirical studies show that the proposed method outperforms other ones.
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Alizadeh, H., Minaei, B., Parvin, H., Moshki, M. (2011). An Asymmetric Criterion for Cluster Validation. In: Mehrotra, K.G., Mohan, C., Oh, J.C., Varshney, P.K., Ali, M. (eds) Developing Concepts in Applied Intelligence. Studies in Computational Intelligence, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21332-8_1
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