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A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering

Authors :
Alokananda Dey
Siddhartha Bhattacharyya
Sandip Dey
Debanjan Konar
Jan Platos
Vaclav Snasel
Leo Mrsic
Pankaj Pal
Source :
Mathematics, Vol 11, Iss 9, p 2018 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

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.

Details

Language :
English
ISSN :
22277390
Volume :
11
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Mathematics
Publication Type :
Academic Journal
Accession number :
edsdoj.b098ce3d46c54fee96e4904679b5035a
Document Type :
article
Full Text :
https://doi.org/10.3390/math11092018