and each provided thoughtful replies to our original article ( ). 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 -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.
-Means Clustering and Mixture Model Clustering
Abstract
and each provided thoughtful replies to our original article ( ). 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 -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.Preview Only. This article cannot be rented because we do not currently have permission from the publisher.
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-Means Clustering and Mixture Model Clustering
Steinley, Douglas; Brusco, Michael J.
Follow Psychological Methods
, Volume 16 (1): 89
PsycARTICLES® – Mar 1, 2011
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