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A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges

Author

Listed:
  • C. Biernacki

    (Université de Lille)

  • J. Jacques

    (Université de Lyon)

  • C. Keribin

    (Université Paris-Saclay, CNRS, Inria, Laboratoire de mathématiques d’Orsay)

Abstract

Model-based co-clustering can be seen as a particularly important extension of model-based clustering. It allows for a significant reduction of both the number of rows (individuals) and columns (variables) of a data set in a parsimonious manner, and also allows interpretability of the resulting reduced data set since the meaning of the initial individuals and features is preserved. Moreover, it benefits from the rich statistical theory for both estimation and model selection. Many works have produced new advances on this topic in recent years, and this paper offers a general update of the related literature. In addition, we advocate two main messages, supported by specific research material: (1) co-clustering requires further research to fix some well-identified estimation issues, and (2) co-clustering is one of the most promising approaches for clustering in the (very) high-dimensional setting, which corresponds to the global trend in modern data sets.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jclass:v:40:y:2023:i:2:d:10.1007_s00357-023-09441-3
    DOI: 10.1007/s00357-023-09441-3
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    References listed on IDEAS

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