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Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures

Published online by Cambridge University Press:  01 January 2025

Douglas Steinley  and
Michael J. Brusco
Douglas Steinley*
Affiliation:
University of Missouri-Columbia
Michael J. Brusco
Affiliation:
Florida State University
*
Requests for reprints should be sent to Douglas Steinley, Department of Psychological Sciences, University of Missouri-Columbia, 210 McAlester Hall, Columbia, MO 65211, USA. E-mail: steinleyd@missouri.edu
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Abstract

Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the performance of nearly all of the variable selection procedures. Overall, a variable selection technique based on a variance-to-range weighting procedure coupled with the largest decreases in within-cluster sums of squares error performed the best. On the other hand, variable selection methods used in conjunction with finite mixture models performed the worst.

Keywords

cluster analysisvariable selection

Information

Type
Theory and Methods
Information
Psychometrika , Volume 73 , Issue 1 , March 2008 , pp. 125 - 144
Copyright
Copyright © 2007 The Psychometric Society

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