Author

• Paul D. McNicholas

Abstract

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.

Keywords

• Classification
• Clustering
• Discriminant analysis
• mclust
• mixture models
• model-based clustering
• model selection
• Parameter estimation
• pgmm