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

• Published in: Advances in data analysis and classification Vol. 10; no. 4; pp. 541 - 562
• Main Authors: Rikhtehgaran, Reyhaneh, Kazemi, Iraj
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2016
• Subjects: Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Economics; Finance; Health Sciences; Humanities; Insurance; Law; Management; Mathematics and Statistics; Medicine; Physics; Regular Article; Statistical Theory and Methods; Statistics; Statistics for Business; Statistics for Engineering; Statistics for Life Sciences; Statistics for Social Sciences; Dirichlet process mixture; 62M99; 62J05; 62G08; Mixed-effects models; Model-based clustering; 62H30; Dirichlet; distribution
• ISSN: 1862-5347; 1862-5355
• DOI: 10.1007/s11634-016-0262-x

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.