Model-based clustering via linear cluster-weighted models

Authors:
Salvatore Ingrassia;Simona C. Minotti;Antonio Punzo
Affiliations:
Department of Economics and Business, University of Catania, Corso Italia 55, 95129 Catania, Italy;Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy;Department of Economics and Business, University of Catania, Corso Italia 55, 95129 Catania, Italy
Venue:
Computational Statistics & Data Analysis
Year:
2014

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Abstract

A novel family of twelve mixture models with random covariates, nested in the linear t cluster-weighted model (CWM), is introduced for model-based clustering. The linear t CWM was recently presented as a robust alternative to the better known linear Gaussian CWM. The proposed family of models provides a unified framework that also includes the linear Gaussian CWM as a special case. Maximum likelihood parameter estimation is carried out within the EM framework, and both the BIC and the ICL are used for model selection. A simple and effective hierarchical-random initialization is also proposed for the EM algorithm. The novel model-based clustering technique is illustrated in some applications to real data. Finally, a simulation study for evaluating the performance of the BIC and the ICL is presented.