Metaheuristic Biclustering Algorithms: From State-of-the-Art to Future Opportunities

José-García, Adán; Jacques, Julie; Sobanski, Vincent; Dhaenens, Clarisse

Type de document :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1145/3617590

Titre :

Metaheuristic Biclustering Algorithms: From State-of-the-Art to Future Opportunities

Auteur(s) :

José-García, Adán [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Jacques, Julie [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sobanski, Vincent [Auteur]

Institut universitaire de France [IUF]
Institute for Translational Research in Inflammation - U 1286 [INFINITE]
Dhaenens, Clarisse [Auteur]

Operational Research, Knowledge And Data [ORKAD]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la revue :

ACM Computing Surveys

Pagination :

1-38

Éditeur :

Association for Computing Machinery

Date de publication :

2023-10-06

ISSN :

0360-0300

Mot(s)-clé(s) en anglais :

metaheuristics
biclustering
co-clustering
subspace clustering

Discipline(s) HAL :

Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Réseau de neurones [cs.NE]

Résumé en anglais : [en]

Biclustering is an unsupervised machine-learning technique that simultaneously clusters rows and columns in a data matrix. Over the past two decades, the field of biclustering has emerged and grown significantly, and ...
Lire la suite >Biclustering is an unsupervised machine-learning technique that simultaneously clusters rows and columns in a data matrix. Over the past two decades, the field of biclustering has emerged and grown significantly, and currently plays an essential role in various applications such as bioinformatics, text mining, and pattern recognition. However, finding significant biclusters in large-scale datasets is an NP-hard problem that can be formulated as an optimization problem. Therefore, metaheuristics have been applied to address biclustering problems due to their (i) ability to efficiently explore search spaces of complex optimization problems, (ii) capability to find solutions in reasonable computation time, and (iii) facility to adapt to different problem formulations as they are considered general-purpose heuristic algorithms. Although several studies on biclustering approaches have been proposed, a comprehensive study using metaheuristics for bicluster analysis is missing. This work presents a survey of metaheuristic approaches to address the biclustering problem in various scientific applications. 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.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

Fichiers

document
Accès libre
Accéder au document

2023-CSUR-survery.pdf
Accès libre
Accéder au document

Metaheuristic Biclustering Algorithms: ... BibTeX CSV Excel RIS

Fichiers

Metaheuristic Biclustering Algorithms: ...

BibTeX

CSV

Excel

RIS