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Comparative Evaluation of Two Superior Stopping Rules for Hierarchical Cluster Analysis

Published online by Cambridge University Press:  01 January 2025

Robert S. Atlas  and
John E. Overall
Robert S. Atlas
Affiliation:
The University of Texas Medical School at Houston
John E. Overall*
Affiliation:
The University of Texas Medical School at Houston
*
Reprints may be requested from John E. Overall, University of Texas-Houston Medical School, Department of Psychiatry and Behavioral Science, P.O. Box 20708, Houston, TX 77225.
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Abstract

A split-sample replication stopping rule for hierarchical cluster analysis is compared with the internal criterion previously found superior by Milligan and Cooper (1985) in their comparison of 30 different procedures. The number and extent of overlap of the latent population distributions was systematically varied in the present evaluation of stopping-rule validity. Equal and unequal population base rates were also considered. Both stopping rules correctly identified the actual number of populations when there was essentially no overlap and clusters occupied visually distinct regions of the measurement space. The replication criterion, which is evaluated by clustering of cluster means from preliminary analyses that are accomplished on random partitions of an original data set, was superior as the degree of overlap in population distributions increased. Neither method performed adequately when overlap obliterated visually discernible density nodes.

Keywords

cluster analysishierarchicalstopping rulecluster distancespopulation overlap

Information

Type
Original Paper
Information
Psychometrika , Volume 59 , Issue 4 , December 1994 , pp. 581 - 591
Copyright
Copyright © 1994 The Psychometric Society

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Footnotes

This research was supported in part by NIMH grant 5R01 MH 32457 14.

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