Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

Autor/inn/en	Steinley, Douglas; Brusco, Michael J.
Titel	Evaluating Mixture Modeling for Clustering: Recommendations and Cautions
Quelle	In: Psychological Methods, 16 (2011) 1, S.63-79 (17 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1082-989X
DOI	10.1037/a0022673
Schlagwörter	Multivariate Analysis; Monte Carlo Methods; Comparative Analysis; Models; Correlation + Suchen Sie Ihr Suchwort? Multivariate Analyse; Monte-Carlo-Methode; Analogiemodell; Korrelation
Abstract	This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison, 2002). Focus is given to the multivariate normal distribution, and 9 separate decompositions (i.e., class structures) of the covariance matrix are investigated. To provide a link to the current literature, comparisons are made with K-means clustering in 3 detailed Monte Carlo studies. The findings have implications for applied researchers in that mixture-model clustering techniques performed best when the covariance structure and number of clusters were known. However, as the information about the shape and number of clusters became unknown, degraded performance was observed for both K-means clustering and mixture-model clustering. (Contains 5 figures, 5 tables and 8 footnotes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10