Probabilistic assessment of model-based clustering

Xuwen Zhu and Volodymyr Melnykov ()

Advances in Data Analysis and Classification, 2015, vol. 9, issue 4, 395-422

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

Keywords: Model-based clustering classification; Influential observations; Diagnostics; Gaussian mixture models; 62H30 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11634-015-0215-9

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