The determination of uncertainty levels in robust clustering of subjects with longitudinal observations using the Dirichlet process mixture

Reyhaneh Rikhtehgaran () and Iraj Kazemi
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Reyhaneh Rikhtehgaran: Isfahan University of Technology
Iraj Kazemi: University of Isfahan

Advances in Data Analysis and Classification, 2016, vol. 10, issue 4, No 7, 562 pages

Abstract: Abstract In this paper we introduce a new method to the cluster analysis of longitudinal data focusing on the determination of uncertainty levels for cluster memberships. The method uses the Dirichlet-t distribution which notably utilizes the robustness feature of the student-t distribution in the framework of a Bayesian semi-parametric approach together with robust clustering of subjects evaluates the uncertainty level of subjects memberships to their clusters. We let the number of clusters and the uncertainty levels be unknown while fitting Dirichlet process mixture models. Two simulation studies are conducted to demonstrate the proposed methodology. The method is applied to cluster a real data set taken from gene expression studies.

Keywords: Dirichlet process mixture; Dirichlet-t distribution; Mixed-effects models; Model-based clustering; 62H30; 62J05; 62G08; 62M99 (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11634-016-0262-x

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