Skip to Main content Skip to Navigation
Journal articles

Co-clustering through Latent Bloc Model: a Review

Abstract : We present here model-based co-clustering methods, with a focus on the latent block model (LBM). We introduce several specifications of the LBM (standard, sparse, Bayesian) and review some identifiability results. We show how the complex dependency structure prevents standard maximum likelihood estimation and present alternative and popular inference methods. Those estimation methods are based on a tractable approximation of the likelihood and rely on iterative procedures, which makes them difficult to analyze. We nevertheless present some asymptotic results for consistency. The results are partial as they rely on a reasonable but still unproved condition. Likewise, available model selection tools for choosing the number of groups in rows and columns are only valid up to a conjecture. We also briefly discuss non model-based co-clustering procedures. Finally, we show how LBM can be used for bipartite graph analysis and highlight throughout this review its connection to the Stochastic Block Model.
Complete list of metadata

Cited literature [38 references]  Display  Hide  Download
Contributor : Vincent Brault Connect in order to contact the contributor
Submitted on : Tuesday, April 2, 2019 - 4:30:22 PM
Last modification on : Monday, August 16, 2021 - 4:36:05 PM
Long-term archiving on: : Wednesday, July 3, 2019 - 5:26:01 PM


474-Texte de l'article-1832-2-...
Explicit agreement for this submission


  • HAL Id : hal-02088216, version 1


Vincent Brault, Mahendra Mariadassou. Co-clustering through Latent Bloc Model: a Review. Journal de la Société Française de Statistique, Société Française de Statistique et Société Mathématique de France, 2015, 156 (3), pp.120-139. ⟨hal-02088216⟩



Record views


Files downloads