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.
McNicholas, P. D. (2022). On Model-Based Clustering, Classification, and Discriminant Analysis. Journal of the Iranian Statistical Society, 10(2), 181–190.
MLA
McNicholas, P. D. "On Model-Based Clustering, Classification, and Discriminant Analysis", Journal of the Iranian Statistical Society, 10, 2, 2022, 181–190.
HARVARD
McNicholas, P. D. (2022). 'On Model-Based Clustering, Classification, and Discriminant Analysis', Journal of the Iranian Statistical Society, 10(2), pp. 181–190.
CHICAGO
McNicholas, P. D., "On Model-Based Clustering, Classification, and Discriminant Analysis." Journal of the Iranian Statistical Society, 10 2 (2022): 181–190,
VANCOUVER
McNicholas P. D. On Model-Based Clustering, Classification, and Discriminant Analysis. JIRSS. 2022;10(2):181–190.
