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Clustering criteria for discrete data and latent class models

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  • Gilles Celeux
  • Gérard Govaert

Abstract

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Suggested Citation

  • Gilles Celeux & Gérard Govaert, 1991. "Clustering criteria for discrete data and latent class models," Journal of Classification, Springer;The Classification Society, vol. 8(2), pages 157-176, December.
    • Handle: RePEc:spr:jclass:v:8:y:1991:i:2:p:157-176
    DOI: 10.1007/BF02616237
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    References listed on IDEAS

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    1. Peter Bryant, 1988. "On characterizing optimization-based clustering methods," Journal of Classification, Springer;The Classification Society, vol. 5(1), pages 81-84, March.
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    Cited by:

    1. Hunt, Lynette A. & Basford, Kaye E., 2016. "Comparing classical criteria for selecting intra-class correlated features in Multimix," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 350-366.
    2. Yang, Miin-Shen & Yu, Nan-Yi, 2005. "Estimation of parameters in latent class models using fuzzy clustering algorithms," European Journal of Operational Research, Elsevier, vol. 160(2), pages 515-531, January.
    3. Christophe Biernacki & Matthieu Marbac & Vincent Vandewalle, 2021. "Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 129-157, April.
    4. Bouguila, Nizar, 2010. "On multivariate binary data clustering and feature weighting," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 120-134, January.
    5. Phipps Arabie, 1991. "Was euclid an unnecessarily sophisticated psychologist?," Psychometrika, Springer;The Psychometric Society, vol. 56(4), pages 567-587, December.
    6. Tin Lok James Ng & Thomas Brendan Murphy, 2022. "Model-based clustering for random hypergraphs," 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. 16(3), pages 691-723, September.
    7. Bock, Hans H., 1996. "Probabilistic models in cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 5-28, November.
    8. Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2016. "Latent class model with conditional dependency per modes to cluster categorical data," 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. 10(2), pages 183-207, June.
    9. Marbac, Matthieu & Sedki, Mohammed, 2017. "A family of block-wise one-factor distributions for modeling high-dimensional binary data," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 130-145.
    10. Paul D. McNicholas, 2016. "Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 331-373, October.
    11. Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2015. "Model-Based Clustering for Conditionally Correlated Categorical Data," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 145-175, July.
    12. Govaert, Gérard & Nadif, Mohamed, 2008. "Block clustering with Bernoulli mixture models: Comparison of different approaches," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3233-3245, February.
    13. Gyllenberg, Mats & Koski, Timo & Verlaan, Martin, 1997. "Classification of Binary Vectors by Stochastic Complexity," Journal of Multivariate Analysis, Elsevier, vol. 63(1), pages 47-72, October.
    14. 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.
    15. Matthieu Marbac & Mohammed Sedki & Tienne Patin, 2020. "Variable Selection for Mixed Data Clustering: Application in Human Population Genomics," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 124-142, April.

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