Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

• Published in: Electronics (Basel) Vol. 10; no. 2; p. 101
• Main Authors: Abualigah, Laith, Gandomi, Amir H., Elaziz, Mohamed Abd, Hamad, Husam Al, Omari, Mahmoud, Alshinwan, Mohammad, Khasawneh, Ahmad M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2021
• Subjects: Algorithms; Artificial intelligence; Big Data; Clustering; Data mining; Datasets; Gene expression; Heuristic; Heuristic methods; Machine learning; Ontology; Optimization; Optimization algorithms; Pattern recognition; Semantics; Sensors; Swarm intelligence
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10020101

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm.