Commentary on Steinley and Brusco (2011): Recommendations and cautions

McLachlan, Geoffrey J. (2011) Commentary on Steinley and Brusco (2011): Recommendations and cautions. Psychological Methods, 16 1: 80-81. doi:10.1037/a0021141

Author McLachlan, Geoffrey J.
Title Commentary on Steinley and Brusco (2011): Recommendations and cautions
Journal name Psychological Methods   Check publisher's open access policy
ISSN 1082-989X
Publication date 2011-03
Sub-type Other
DOI 10.1037/a0021141
Volume 16
Issue 1
Start page 80
End page 81
Total pages 2
Place of publication Washington, DC, United States
Publisher American Psychological Association
Language eng
Abstract I discuss the recommendations and cautions in Steinley and Brusco’s (2011) article on the use of finite models to cluster a data set. In their article, much use is made of comparison with the K-means procedure. As noted by researchers for over 30 years, the K-means procedure can be viewed as a special case of finite mixture modeling in which the components are in equal (fixed) proportions and are taken to be normal with a common spherical covariance matrix. In this commentary, I pay particular attention to this link and to the use of normal mixture models with arbitrary component-covariance matrices.
Keyword Normal mixture models
Expectation-maximization algorithm
Mixtures of factor analyzers
Q-Index Code CX
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Published under 'COMMENT'. Steinley, D., & Brusco, M. J. (2011). Evaluating mixture modeling for clustering: Recommendations and cautions. Psychological Methods, 16, 63–79.

Document type: Journal Article
Sub-type: Other
Collections: School of Mathematics and Physics
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Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
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Created: Sat, 28 May 2011, 16:58:55 EST by Professor Geoff Mclachlan on behalf of Mathematics