"K"-Means Clustering and Mixture Model Clustering: Reply to McLachlan (2011) and Vermunt (2011)

Autor/inn/en	Steinley, Douglas; Brusco, Michael J.
Titel	"K"-Means Clustering and Mixture Model Clustering: Reply to McLachlan (2011) and Vermunt (2011)
Quelle	In: Psychological Methods, 16 (2011) 1, S.89-92 (4 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1082-989X
DOI	10.1037/a0022679
Schlagwörter	Stellungnahme; Multivariate Analysis; Research Methodology; Data; Models; Mathematics; Regression (Statistics) + Suchen Sie Ihr Suchwort? Multivariate Analyse; Research method; Forschungsmethode; Daten; Analogiemodell; Mathematik; Regression; Regressionsanalyse
Abstract	McLachlan (2011) and Vermunt (2011) each provided thoughtful replies to our original article (Steinley & Brusco, 2011). This response serves to incorporate some of their comments while simultaneously clarifying our position. We argue that greater caution against overparamaterization must be taken when assuming that clusters are highly elliptical in nature. Specifically, users of mixture model clustering techniques should be wary of overreliance on fit indices, and the importance of cross-validation is highlighted. Additionally, we note that "K"-means clustering is part of a larger family of discrete partitioning algorithms, many of which are designed to solve problems identical to those for which mixture modeling approaches are often touted. (Contains 3 footnotes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10