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Model-based clustering via linear cluster-weighted models

Abstract

A novel family of twelve mixture models with random covariates, nested in the linear tt cluster-weighted model (CWM), is introduced for model-based clustering. The linear tt 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.

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