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Statistics Surveys 2010
Finite mixture models and model-based clusteringKeywords: EM algorithm , Model selection , Variable selection , Diagnostics , Two-dimensional gel electrophoresis data , Proteomics , Text mining , Magnitude magnetic resonance images Abstract: Finite mixture models have a long history in statistics, having been used to model population heterogeneity, generalize distributional assumptions, and lately, for providing a convenient yet formal framework for clustering and classification. This paper provides a detailed review into mixture models and model-based clustering. Recent trends as well as open problems in the area are also discussed.
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