Biclustering Algorithms Based on Metaheuristics: A Review

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