A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering

Alokananda Dey, Siddhartha Bhattacharyya (), Sandip Dey, Debanjan Konar, Jan Platos, Vaclav Snasel, Leo Mrsic and Pankaj Pal
Additional contact information
Alokananda Dey: RCC Institute of Information Technology, Kolkata 700015, West Bengal, India
Siddhartha Bhattacharyya: Rajnagar Mahavidyalaya, Rajnagar 731130, Birbhum, India
Sandip Dey: Sukanta Mahavidyalaya, Dhupguri 735210, Jalpaiguri, India
Debanjan Konar: Center for Advanced Systems Understanding (CASUS), Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 02826 Görlitz, Germany
Jan Platos: Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, 70800 Poruba-Ostrava, Czech Republic
Vaclav Snasel: Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, 70800 Poruba-Ostrava, Czech Republic
Leo Mrsic: Department of Data Analysis, Algebra University College, Catholic University of Croatia, 10000 Zagreb, Croatia
Pankaj Pal: RCC Institute of Information Technology, Kolkata 700015, West Bengal, India

Mathematics, 2023, vol. 11, issue 9, 1-44

Abstract: In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clustering algorithms for this purpose has been contemplated by some researchers. Several automatic clustering algorithms assisted by quantum-inspired metaheuristics have been developed in recent years. However, the literature lacks definitive documentation of the state-of-the-art quantum-inspired metaheuristic algorithms for automatically clustering datasets. This article presents a brief overview of the automatic clustering process to establish the importance of making the clustering process automatic. The fundamental concepts of the quantum computing paradigm are also presented to highlight the utility of quantum-inspired algorithms. This article thoroughly analyses some algorithms employed to address the automatic clustering of various datasets. The reviewed algorithms were classified according to their main sources of inspiration. In addition, some representative works of each classification were chosen from the existing works. Thirty-six such prominent algorithms were further critically analysed based on their aims, used mechanisms, data specifications, merits and demerits. Comparative results based on the performance and optimal computational time are also presented to critically analyse the reviewed algorithms. As such, this article promises to provide a detailed analysis of the state-of-the-art quantum-inspired metaheuristic algorithms, while highlighting their merits and demerits.

Keywords: automatic clustering; metaheuristics; quantum computing; quantum-inspired metaheuristics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/9/2018/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/9/2018/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:9:p:2018-:d:1131434

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().