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ERIC Number: EJ917495
Record Type: Journal
Publication Date: 2011-Mar
Pages: 4
Abstractor: As Provided
Reference Count: 40
ISBN: N/A
ISSN: ISSN-1082-989X
"K"-Means Clustering and Mixture Model Clustering: Reply to McLachlan (2011) and Vermunt (2011)
Steinley, Douglas; Brusco, Michael J.
Psychological Methods, v16 n1 p89-92 Mar 2011
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.)
American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications
Publication Type: Journal Articles; Opinion Papers
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A