Volume 10, Issue 2 (November 2011)                   JIRSS 2011, 10(2): 181-190 | Back to browse issues page

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Abstract:   (10281 Views)
The use of mixture models for clustering and classification has burgeoned into an important subfield of multivariate analysis. These approaches have been around for a half-century or so, with significant activity in the area over the past decade. The primary focus of this paper is to review work in model-based clustering, classification, and discriminant analysis, with particular attention being paid to two techniques that can be implemented using respective R packages. Parameter estimation and model selection are also discussed. The paper concludes with a summary, discussion, and some thoughts on future work.
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Received: 2011/11/7 | Accepted: 2015/09/12