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Enhancing the selection of a model-based clustering with external categorical variables

Author

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  • Jean-Patrick Baudry
  • Margarida Cardoso
  • Gilles Celeux
  • Maria Amorim
  • Ana Ferreira

Abstract

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Jean-Patrick Baudry & Margarida Cardoso & Gilles Celeux & Maria Amorim & Ana Ferreira, 2015. "Enhancing the selection of a model-based clustering with external categorical variables," 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. 9(2), pages 177-196, June.
    • Handle: RePEc:spr:advdac:v:9:y:2015:i:2:p:177-196
    DOI: 10.1007/s11634-014-0177-3
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    References listed on IDEAS

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    1. Christian Hennig & Tim F. Liao, 2013. "How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(3), pages 309-369, May.
    2. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard & Langrognet, Florent, 2006. "Model-based cluster and discriminant analysis with the MIXMOD software," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 587-600, November.
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    Cited by:

    1. Adrian O’Hagan & Arthur White, 2019. "Improved model-based clustering performance using Bayesian initialization averaging," Computational Statistics, Springer, vol. 34(1), pages 201-231, March.
    2. Jiyeon Song & Seung Jun Shin, 2018. "Stability approach to selecting the number of principal components," Computational Statistics, Springer, vol. 33(4), pages 1923-1938, December.
    3. Marek Śmieja & Magdalena Wiercioch, 2017. "Constrained clustering with a complex cluster structure," 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. 11(3), pages 493-518, September.
    4. Ferraro, Maria Brigida & Giordani, Paolo & Vichi, Maurizio, 2021. "A class of two-mode clustering algorithms in a fuzzy setting," Econometrics and Statistics, Elsevier, vol. 18(C), pages 63-78.

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