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Classification and Mixture Approaches to Clustering Via Maximum Likelihood

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  • S. Ganesalingam

Abstract

Mixtures of distributions, in particular the normal distribution, have been used extensively as models in a wide variety of important practical situations where the population of interest may be considered to consist of two or more subpopulations mixed in varying proportions. The problem of decomposing such a mixture of distributions is of considerable interest and utility. 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 the two possible populations. The basic assumptions and associated properties of the two methods are contrasted and illustrated by a series of simulations under two different sampling schemes, namely the mixture sampling scheme and the separate sampling scheme. A case study is presented to demonstrate the basic differences between these two methods.

Suggested Citation

  • S. Ganesalingam, 1989. "Classification and Mixture Approaches to Clustering Via Maximum Likelihood," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 38(3), pages 455-466, November.
  • Handle: RePEc:bla:jorssc:v:38:y:1989:i:3:p:455-466
    DOI: 10.2307/2347733
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    Cited by:

    1. Govaert, G. & Nadif, M., 1996. "Comparison of the mixture and the classification maximum likelihood in cluster analysis with binary data," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 65-81, November.
    2. LuĂ­sa Novais & Susana Faria, 2021. "Comparison of the EM, CEM and SEM algorithms in the estimation of finite mixtures of linear mixed models: a simulation study," Computational Statistics, Springer, vol. 36(4), pages 2507-2533, December.

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