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An Examination of the Effect of Six Types of Error Perturbation on Fifteen Clustering Algorithms

Published online by Cambridge University Press:  01 January 2025

Glenn W. Milligan
Glenn W. Milligan*
Affiliation:
The Ohio State University
*
Requests for reprints should be sent to Glenn W. Milligan, Faculty of Management Sciences, 356 Hagerty Hall, The Ohio State University, Columbus, Ohio 43210.
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Abstract

An evaluation of several clustering methods was conducted. Artificial clusters which exhibited the properties of internal cohesion and external isolation were constructed. The true cluster structure was subsequently hidden by six types of error-perturbation. The results indicated that the hierarchical methods were differentially sensitive to the type of error perturbation. In addition, generally poor recovery performance was obtained when random seed points were used to start the K-means algorithms. However, two alternative starting procedures for the nonhierarchical methods produced greatly enhanced cluster recovery and were found to be robust with respect to all of the types of error examined.

Keywords

clustering algorithmsclustering validationMonte Carlo research

Information

Type
Original Paper
Information
Psychometrika , Volume 45 , Issue 3 , September 1980 , pp. 325 - 342
Copyright
Copyright © 1980 The Psychometric Society

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References

Reference Notes

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