Psychometrika

, Volume 45, Issue 3, pp 325–342

An examination of the effect of six types of error perturbation on fifteen clustering algorithms

  • Glenn W. Milligan
Article

DOI: 10.1007/BF02293907

Cite this article as:
Milligan, G.W. Psychometrika (1980) 45: 325. doi:10.1007/BF02293907

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 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.

Key words

clustering algorithmsclustering validationMonte Carlo research

Copyright information

© The Psychometric Society 1980

Authors and Affiliations

  • Glenn W. Milligan
    • 1
  1. 1.Faculty of Management SciencesThe Ohio State UniversityColumbus