Biclustering Algorithms Based on Metaheuristics: A Review

Adán José-García (1, 2) , Julie Jacques (1, 2) , Vincent Sobanski (3, 4) , Clarisse Dhaenens (1, 2)

1 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
2 ORKAD - Operational Research, Knowledge And Data
3 INFINITE (Ex-Liric) - Institute for Translational Research in Inflammation - U 1286
4 IUF - Institut universitaire de France

Biclustering is an unsupervised machine learning technique that simultaneously clusters rows and columns in a data matrix. Biclustering has emerged as an important approach and plays an essential role in various applications such as bioinformatics, text mining, and pattern recognition. However, finding significant biclusters is an NP-hard problem that can be formulated as an optimization problem. Therefore, different metaheuristics have been applied to biclustering problems because of their exploratory capability of solving complex optimization problems in reasonable computation time. Although various surveys on biclustering have been proposed, there is a lack of a comprehensive survey on the biclustering problem using metaheuristics. This chapter will present a survey of metaheuristics approaches to address the biclustering problem. The review focuses on the underlying optimization methods and their main search components: representation, objective function, and variation operators. A specific discussion on single versus multi-objective approaches is presented. Finally, some emerging research directions are presented.

Mots clés: Biclustering; Unsupervised learning; Metaheuristics

Domaines: Apprentissage [cs.LG]

Adán José-García, Julie Jacques, Vincent Sobanski, Clarisse Dhaenens. Biclustering Algorithms Based on Metaheuristics: A Review. Mansour Eddaly; Bassem Jarboui; Patrick Siarry. Metaheuristics for Machine Learning. New Advances and Tools, Springer Nature Singapore, pp.39-71, 2023, Computational Intelligence Methods and Applications, ⟨10.1007/978-981-19-3888-7_2⟩. ⟨hal-03790510⟩