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A Case Study of two Clustering Methods based on Maximum Likelihood

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  • S. Ganesalingam
  • G. J. McLachlan

Abstract

Two commonly used clustering methods based on maximum likelihood are considered in the context of the classification problem where observations of unknown origin belong to one of two possible populations. The basic assumptions and associated properties of the two methods are contrasted and illustrated by their application to some medical data. Also, the problem of updating an allocation procedure is considered.

Suggested Citation

  • S. Ganesalingam & G. J. McLachlan, 1979. "A Case Study of two Clustering Methods based on Maximum Likelihood," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 33(2), pages 81-90, June.
    • Handle: RePEc:bla:stanee:v:33:y:1979:i:2:p:81-90
    DOI: 10.1111/j.1467-9574.1979.tb00665.x
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    Cited by:

    1. Cinzia Viroli, 2010. "Dimensionally Reduced Model-Based Clustering Through Mixtures of Factor Mixture Analyzers," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 363-388, November.

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