Volume 10, Issue 2 (November 2011)
JIRSS 2011, 10(2): 181-190 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
McNicholas P D. On Model-Based Clustering, Classification, and Discriminant Analysis. JIRSS. 2011; 10 (2) :181-190
URL: http://jirss.irstat.ir/article-1-163-en.html
URL: http://jirss.irstat.ir/article-1-163-en.html
Abstract: (10173 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.
Keywords: Classification, clustering, discriminant analysis, mclust, mixture models, model-based clustering, model selection, parameter estimation, pgmm
Received: 2011/11/7 | Accepted: 2015/09/12
