评 Steinley 和 Brusco（2011）：建议与警示。

Commentary on Steinley and Brusco (2011): recommendations and cautions.

机构信息

Department of Mathematics and Institute for Molecular Bioscience, University of Queensland, St. Lucia, Brisbane 4072, Queensland, Australia.

出版信息

Psychol Methods. 2011 Mar;16(1):80-1; discussion 89-92. doi: 10.1037/a0021141.

DOI:10.1037/a0021141

PMID:21381818

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.

摘要

我讨论了 Steinley 和 Brusco（2011）文章中关于使用有限模型对数据集进行聚类的建议和注意事项。在他们的文章中，大量使用了与 K-均值程序的比较。正如 30 多年来研究人员所指出的，K-均值程序可以看作是有限混合模型的一个特例，其中各分量的比例相等（固定），并被认为是具有公共球形协方差矩阵的正态分布。在本评论中，我特别关注这一联系以及使用具有任意分量协方差矩阵的正态混合模型。