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

Abstract

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.