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K-modes Clustering

Author

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  • Anil Chaturvedi
  • Paul E. Green
  • J. Douglas Caroll

Abstract

We norm (defined as the limit of an L p norm as p approaches zero). In Monte Carlo simulations, both K-modes and the latent class procedures (e.g., Goodman 1974) performed with equal efficiency in recovering a known underlying cluster structure. However, K-modes is an order of magnitude faster than the latent class procedure in speed and suffers from fewer problems of local optima than do the latent class procedures. For data sets involving a large number of categorical variables, latent class procedures become computationally extremly slow and hence infeasible. We conjecture that, although in some cases latent class procedures might perform better than K-modes, it could out-perform latent class procedures in other cases. Hence, we recommend that these two approaches be used as "complementary" procedures in performing cluster analysis. We also present an empirical comparison of K-modes and latent class, where the former method prevails. Copyright Springer-Verlag New York Inc. 2001

Suggested Citation

  • Anil Chaturvedi & Paul E. Green & J. Douglas Caroll, 2001. "K-modes Clustering," Journal of Classification, Springer;The Classification Society, vol. 18(1), pages 35-55, January.
    • Handle: RePEc:spr:jclass:v:18:y:2001:i:1:p:35-55
    DOI: 10.1007/s00357-001-0004-3
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    Cited by:

    1. Isabella Morlini, 2012. "A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(1), pages 5-28, April.
    2. Joachim Harloff, 2011. "Extracting cover sets from free fuzzy sorting data," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(6), pages 1445-1457, October.
    3. Viviana Amati & Silvia Meggiolaro & Giulia Rivellini & Susanna Zaccarin, 2017. "Relational Resources of Individuals Living in Couple: Evidence from an Italian Survey," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 134(2), pages 547-590, November.
    4. Isabella Morlini & Sergio Zani, 2012. "A New Class of Weighted Similarity Indices Using Polytomous Variables," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 199-226, July.
    5. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    6. Willem Heiser, 2013. "In memoriam, J. Douglas Carroll 1939–2011," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 5-13, January.

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