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.
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Alizadeh, H., Minaei-Bidgoli, B., Parvin, H.: A New Criterion for Clusters Validation. In: Artificial Intelligence Applications and Innovations (AIAI 2011). LNCS. Springer, Heidelberg (in press 2011) ISSN: 0302-9743
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Parvin, H., Minaei, B., Parvin, S. (2011). A Metric to Evaluate a Cluster by Eliminating Effect of Complement Cluster. In: Bach, J., Edelkamp, S. (eds) KI 2011: Advances in Artificial Intelligence. KI 2011. Lecture Notes in Computer Science(), vol 7006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24455-1_23
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DOI: https://doi.org/10.1007/978-3-642-24455-1_23
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