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Keywords:

  • model-based clustering;
  • finite mixture models;
  • EM algorithm;
  • initialization;
  • dimensionality reduction

Abstract

Model-based clustering is an increasingly popular area of cluster analysis that relies on probabilistic description of data by means of finite mixture models. Mixture distributions prove to be a powerful technique for modeling heterogeneity in data. In model-based clustering, each data group is seen as a sample from one or several mixture components. Despite attractive interpretation, model-based clustering poses many challenges. This paper discusses some of the most important problems a researcher might encounter while applying the model-based cluster analysis. WIREs Comput Stat 2013, 5:135–148. doi: 10.1002/wics.1248