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Probabilistic assessment of model-based clustering

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  • Xuwen Zhu
  • Volodymyr Melnykov

Abstract

Finite mixtures provide a powerful tool for modeling heterogeneous data. Model-based clustering is a broadly used grouping technique that assumes the existence of the one-to-one correspondence between clusters and mixture model components. Although there are many directions of active research in the model-based clustering framework, very little attention has been paid to studying the specific nature of detected clustering solutions. In this paper, we develop an approach for assessing the variability in classifications carried out by the Bayes decision rule. The proposed technique allows assessing significance of each assignment made. We also apply the developed instrument for identifying influential observations that have impact on the structure of the detected partitioning. The proposed diagnostic methodology is studied and illustrated on synthetic data and applied to the analysis of three well-known classification datasets. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Xuwen Zhu & Volodymyr Melnykov, 2015. "Probabilistic assessment of model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 395-422, December.
    • Handle: RePEc:spr:advdac:v:9:y:2015:i:4:p:395-422
    DOI: 10.1007/s11634-015-0215-9
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    References listed on IDEAS

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    Cited by:

    1. Khadidja Henni & Pierre-Yves Louis & Brigitte Vannier & Ahmed Moussa, 2020. "Is-ClusterMPP: clustering algorithm through point processes and influence space towards high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(3), pages 543-570, September.
    2. Xuwen Zhu, 2019. "Probability of misclassification in model-based clustering," Computational Statistics, Springer, vol. 34(3), pages 1427-1442, September.

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