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Erschienen in:
2011 | OriginalPaper | Buchkapitel
A New Criterion for Clusters Validation
verfasst von : Hosein Alizadeh, Behrouz Minaei, Hamid Parvin
Erschienen in: Artificial Intelligence Applications and Innovations
Verlag: Springer Berlin Heidelberg
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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. The clusters which satisfy a threshold of this measure are selected to participate in clustering ensemble. For combining the chosen clusters, a co-association based consensus function is applied. Since the Evidence Accumulation Clustering 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.