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Model-based clustering via linear cluster-weighted models

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  • Ingrassia, Salvatore
  • Minotti, Simona C.
  • Punzo, Antonio

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.

Suggested Citation

  • Ingrassia, Salvatore & Minotti, Simona C. & Punzo, Antonio, 2014. "Model-based clustering via linear cluster-weighted models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 159-182.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:159-182
    DOI: 10.1016/j.csda.2013.02.012
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    References listed on IDEAS

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