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An Examination of the Effect of Six Types of Error Perturbation on Fifteen Clustering Algorithms
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.
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Cited by
NbClust : An R Package for Determining the Relevant Number of Clusters in a Data Set
Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014) · Journal of Statistical Software · doi:10.18637/jss.v061.i06 · Cited by 3,396
Arnold, M. J., & Reynolds, K. E. (2003) · Journal of Retailing · doi:10.1016/s0022-4359(03)00007-1 · Cited by 2,373
The use and reporting of cluster analysis in health psychology: A review
Clatworthy, J., Buick, D., Hankins, M., Weinman, J., & Horne, R. (2005) · British Journal of Health Psychology · doi:10.1348/135910705x25697 · Cited by 598
Hack, T. F., & Degner, L. F. (2004) · Psycho-Oncology · doi:10.1002/pon.739 · Cited by 244
Comparing test searches in PubMed and Google Scholar
Shultz, M. (2007) · Journal of the Medical Library Association : JMLA · doi:10.3163/1536-5050.95.4.442 · Cited by 174
Metadata
- Authors
- Milligan, G. W.
- Venue
- Psychometrika
- Publisher
- Cambridge University Press (CUP)
- Published
- 1980-09
- Volume
- 45
- Issue
- 3
- Pages
- 325-342
- ISSN
- 0033-3123 (ISSN-L)
- 1860-0980
- OpenAlex
- W2088879945
- Cited by
- 1,162
- References
- 0
- FWCI
- 7.01
- Type
- journal article
- Language
- en
- Open access
- closed
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