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blockcluster: An R Package for Model-Based Co-Clustering

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  • Bhatia, Parmeet Singh
  • Iovleff, Serge
  • Govaert, Gérard

Abstract

Simultaneous clustering of rows and columns, usually designated by bi-clustering, coclustering or block clustering, is an important technique in two way data analysis. A new standard and efficient approach has been recently proposed based on the latent block model (Govaert and Nadif 2003) which takes into account the block clustering problem on both the individual and variable sets. This article presents our R package blockcluster for co-clustering of binary, contingency and continuous data based on these very models. In this document, we will give a brief review of the model-based block clustering methods, and we will show how the R package blockcluster can be used for co-clustering.

Suggested Citation

  • Bhatia, Parmeet Singh & Iovleff, Serge & Govaert, Gérard, 2017. "blockcluster: An R Package for Model-Based Co-Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i09).
    • Handle: RePEc:jss:jstsof:v:076:i09
    DOI: http://hdl.handle.net/10.18637/jss.v076.i09
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    References listed on IDEAS

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    1. 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.
    2. Hathaway, Richard J., 1986. "Another interpretation of the EM algorithm for mixture distributions," Statistics & Probability Letters, Elsevier, vol. 4(2), pages 53-56, March.
    3. Celeux, Gilles & Govaert, Gerard, 1992. "A classification EM algorithm for clustering and two stochastic versions," Computational Statistics & Data Analysis, Elsevier, vol. 14(3), pages 315-332, October.
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    Cited by:

    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.
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    3. Zaheer Ahmed & Alberto Cassese & Gerard Breukelen & Jan Schepers, 2023. "E-ReMI: Extended Maximal Interaction Two-mode Clustering," Journal of Classification, Springer;The Classification Society, vol. 40(2), pages 298-331, July.
    4. Vincent Brault & Antoine Channarond, 2024. "Fast and consistent algorithm for the latent block model," Computational Statistics, Springer, vol. 39(3), pages 1621-1657, May.
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    6. Selosse, Margot & Jacques, Julien & Biernacki, Christophe, 2020. "Model-based co-clustering for mixed type data," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    7. Gong, Tingnan & Zhang, Weiping & Chen, Yu, 2023. "Uncovering block structures in large rectangular matrices," Journal of Multivariate Analysis, Elsevier, vol. 198(C).

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