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Model-based co-clustering for mixed type data

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  • Selosse, Margot
  • Jacques, Julien
  • Biernacki, Christophe

Abstract

The importance of clustering for creating groups of observations is well known. The emergence of high-dimensional data sets with a huge number of features leads to co-clustering techniques, and several methods have been developed for simultaneously producing groups of observations and features. By grouping the data set into blocks (the crossing of a row-cluster and a column-cluster), these techniques can sometimes better summarize the data set and its inherent structure. The Latent Block Model (LBM) is a well-known method for performing co-clustering. However, recently, contexts with features of different types (here called mixed type data sets) are becoming more common. The LBM is not directly applicable to this kind of data set. Here a natural extension of the usual LBM to the “Multiple Latent Block Model” (MLBM) is proposed in order to handle mixed type data sets. Inference is performed using a Stochastic EM-algorithm that embeds a Gibbs sampler, and allows for missing data situations. A model selection criterion is defined to choose the number of row and column clusters. The method is then applied to both simulated and real data sets.

Suggested Citation

  • Selosse, Margot & Jacques, Julien & Biernacki, Christophe, 2020. "Model-based co-clustering for mixed type data," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  • Handle: RePEc:eee:csdana:v:144:y:2020:i:c:s016794731930221x
    DOI: 10.1016/j.csda.2019.106866
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    References listed on IDEAS

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    1. Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2017. "Model-based clustering of Gaussian copulas for mixed data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(23), pages 11635-11656, December.
    2. Damien McParland & Isobel Claire Gormley, 2016. "Model based clustering for mixed data: clustMD," 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 155-169, June.
    3. Gérard Govaert & Mohamed Nadif, 2018. "Mutual information, phi-squared and model-based co-clustering for contingency tables," 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. 12(3), pages 455-488, September.
    4. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    5. Jacques, Julien & Biernacki, Christophe, 2018. "Model-based co-clustering for ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 101-115.
    6. Margot Selosse & Julien Jacques & Christophe Biernacki & Florence Cousson‐Gélie, 2019. "Analysing a quality‐of‐life survey by using a coclustering model for ordinal data and some dynamic implications," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(5), pages 1327-1349, November.
    7. Christophe Biernacki & Alexandre Lourme, 2019. "Unifying data units and models in (co-)clustering," 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. 13(1), pages 7-31, March.
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    1. C. Biernacki & J. Jacques & C. Keribin, 2023. "A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 332-381, July.
    2. Alessandro Casa & Charles Bouveyron & Elena Erosheva & Giovanna Menardi, 2021. "Co-clustering of Time-Dependent Data via the Shape Invariant Model," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 626-649, October.

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