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On determining efficient finite mixture models with compact and essential components for clustering data
2013
Egyptian Informatics Journal
In this paper, an algorithm is proposed to learn and evaluate different finite mixture models (FMMs) for data clustering using a new proposed criterion. The FMM corresponds to the minimum value of the proposed criterion is considered the most efficient FMM with compact and essential components for clustering an input data. The proposed algorithm is referred to as the EMCE algorithm in this paper. The selected FMM by the EMCE algorithm is efficient, in terms of its complexity and composed of
doi:10.1016/j.eij.2013.02.002
fatcat:4hhc2uo6njhmhnz7ft3wzurps4