An examination of the effect of six types of error perturbation on fifteen clustering algorithms
- Cite this article as:
- Milligan, G.W. Psychometrika (1980) 45: 325. doi:10.1007/BF02293907
- 868 Views
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 theK-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.