Advertisement

The Recovery Performance of Two-mode Clustering Methods: Monte Carlo Experiment

  • Sabine Krolak-Schwerdt
  • Michael Wiedenbeck
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In this paper, a Monte Carlo study on the performance of two-mode cluster methods is presented. The synthetical data sets were generated to correspond to two types of data consisting of overlapping as well as disjoint clusters. Furthermore, the data sets differed in cluster number, degrees of within-group homogeneity and between-group heterogeneity as well as degree of cluster overlap. We found that the methods performed very differently depending on type of data, number of clusters, homogeneity and cluster overlap.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. BAIER, D., GAUL, W. and SCHADER, M. (1997): Two-Mode Overlapping Clustering with Applications to Simultaneous Benefit Segmentation and Market Structuring. In: R. Klar and O. Opitz (Eds.): Classification and Knowledge Organization. Springer, Berlin, 557–566.Google Scholar
  2. COLLINS, L.M. and DENT, C.W. (1988): Omega: A general formulation of the Rand index of cluster recovery suitable for non-disjoint solutions. Multivariate Behavioral Research, 23, 231–342.CrossRefGoogle Scholar
  3. DESARBO, W.S. (1982): Gennclus: New Models for General Nonhierarchical Clustering Analysis. Psychometrika, 47, 449–475.zbMATHMathSciNetGoogle Scholar
  4. ECKES, T. and ORLIK, P. (1993): An error variance approach to two-mode hierarchical clustering. Journal of Classification, 10, 51–74.Google Scholar
  5. HARTIGAN, J. (1975): Clustering algorithms. New York: Wiley.Google Scholar
  6. HUBERT, L. and ARABIE, P. (1985): Comparing partitions. Journal of Classification, 2, 193–218.CrossRefGoogle Scholar
  7. KROLAK-SCHWERDT, S. (2003): Two-mode clustering methods: Compare and contrast. In: M. Schader, W. Gaul and M. Vicchi (Eds.): Between data science and everyday web practice. Berlin: Springer, 270–279.Google Scholar
  8. SCHWAIGER, M. (1997): Two-mode classification in advertising research. In: R. Klar and O. Opitz (Eds.): Classification and knowledge Organization. Springer, Berlin, 596–603.Google Scholar
  9. VAN MECHELEN, I., BOCK, H. and DE BOECK, P. (2004): Two-mode clustering methods: A structure overview. Statistical Methods in Medical Research, 13, 363–394.Google Scholar

Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Sabine Krolak-Schwerdt
    • 1
  • Michael Wiedenbeck
    • 2
  1. 1.Department of PsychologySaarland UniversityGermany
  2. 2.Centre for Survey Research and MethodologyMannheimGermany

Personalised recommendations