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An Examination of Procedures for Determining the Number of Clusters in a Data Set

Milligan, Glenn W., Cooper, Martha C.·1985·Psychometrika
DOI 10.1007/bf02294245
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Abstract

A Monte Carlo evaluation of 30 procedures for determining the number of clusters was conducted on artificial data sets which contained either 2, 3, 4, or 5 distinct nonoverlapping clusters. To provide a variety of clustering solutions, the data sets were analyzed by four hierarchical clustering methods. External criterion measures indicated excellent recovery of the true cluster structure by the methods at the correct hierarchy level. Thus, the clustering present in the data was quite strong. The simulation results for the stopping rules revealed a wide range in their ability to determine the correct number of clusters in the data. Several procedures worked fairly well, whereas others performed rather poorly. Thus, the latter group of rules would appear to have little validity, particularly for data sets containing distinct clusters. Applied researchers are urged to select one or more of the better criteria. However, users are cautioned that the performance of some of the criteria may be data dependent.

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Cited by

Metadata

Authors
Milligan, G. W., & Cooper, M. C.
Venue
Psychometrika
Publisher
Cambridge University Press (CUP)
Published
1985-06
Volume
50
Issue
2
Pages
159-179
ISSN
  • 0033-3123 (ISSN-L)
  • 1860-0980
DOI
10.1007/bf02294245
OpenAlex
W1975152892
Cited by
3,868
References
0
FWCI
14.50
Type
journal article
Language
en
Open access
closed
Licence
www.cambridge.org/core/terms (unspecified, 14459 d)

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