A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles (Review Article) Journal of Classification Year : 2023

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

Christophe Biernacki
  • Function : Author
  • PersonId : 923939
Julien Jacques
C. Keribin

Abstract

Model-based co-clustering can be seen as a particularly valuable extension of model-based clustering for three main reasons: (1) while allowing parsimoniously a drastic reduction of both the number of lines/individuals and columns/variables of a data set, (2) it also allows interpretability of such a resulting reduced data set since initial individuals and features meaning is preserved in this latter; (3) moreover it benefits from the powerful mathematical statistics theory for both estimation and model selection. Hence, many authors produced new advances on this topic in the recent years, and this paper offers a general update of the related literature. In addition, it is the opportunity to pass two messages, supported by specific research materials: (1) co-clustering still requires some new and motivating researches for fixing some well-identified estimation issues, (2) co-clustering is probably one of the most promising clustering approach to be addressed in the (very) high dimension setting, which corresponds to the global trend on modern data sets
Fichier principal
Vignette du fichier
Coclustering_survey.pdf (2.91 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03769727 , version 1 (05-09-2022)

Licence

Attribution

Identifiers

  • HAL Id : hal-03769727 , version 1

Cite

Christophe Biernacki, Julien Jacques, C. Keribin. A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges. Journal of Classification, 2023. ⟨hal-03769727⟩
81 View
160 Download

Share

Gmail Facebook X LinkedIn More