A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering

被引:7
作者
Dey, Alokananda [1 ]
Bhattacharyya, Siddhartha [2 ,3 ]
Dey, Sandip [4 ]
Konar, Debanjan [5 ]
Platos, Jan [6 ]
Snasel, Vaclav [6 ]
Mrsic, Leo [3 ,7 ]
Pal, Pankaj [1 ]
机构
[1] RCC Inst Informat Technol, Kolkata 700015, West Bengal, India
[2] Rajnagar Mahavidyalaya, Rajnagar 731130, Birbhum, India
[3] Catholic Univ Croatia, Algebra Univ Coll, Dept Data Anal, Zagreb 10000, Croatia
[4] Sukanta Mahavidyalaya, Jalpaiguri 735210, India
[5] Helmholtz Zentrum Dresden Rossendorf HZDR, Ctr Adv Syst Understanding CASUS, D-02826 Gorlitz, Germany
[6] VSB Tech Univ Ostrava, Fac Elect Engn & Comp Sci, Poruba Ostrava 70800, Czech Republic
[7] Rudolfovo Sci & Technol Ctr, Publ Res Inst, Novo Mesto 8000, Slovenia
关键词
automatic clustering; metaheuristics; quantum computing; quantum-inspired metaheuristics; MULTIOBJECTIVE GENETIC ALGORITHM; PARTICLE SWARM OPTIMIZATION; FUZZY C-MEANS; EVOLUTIONARY ALGORITHM; DIFFERENTIAL EVOLUTION; VALIDITY INDEX; PERFORMANCE EVALUATION; MEAN SHIFT; CLASSIFICATION; CANCER;
D O I
10.3390/math11092018
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
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页数:44
相关论文
共 283 条
[1]  
Abbass H. A., 2002, International Journal on Artificial Intelligence Tools (Architectures, Languages, Algorithms), V11, P531, DOI 10.1142/S0218213002001039
[2]  
Abd Elaziz M, 2019, IEEE C EVOL COMPUTAT, P2315, DOI [10.1109/cec.2019.8790361, 10.1109/CEC.2019.8790361]
[3]  
Abu Abbas O, 2008, INT ARAB J INF TECHN, V5, P320
[4]  
Abubaker A, 2015, PLOS ONE, V10, DOI [10.1371/journal.pone.0130995, 10.1371/journal.pone.0135641]
[5]   Implementing evolutionary optimization on actual quantum processors [J].
Acampora, Giovanni ;
Vitiello, Autilia .
INFORMATION SCIENCES, 2021, 575 :542-562
[6]   Automatic Data Clustering Using Hybrid Firefly Particle Swarm Optimization Algorithm [J].
Agbaje, Moyinoluwa B. ;
Ezugwu, Absalom E. ;
Els, Rosanne .
IEEE ACCESS, 2019, 7 :184963-184984
[7]  
Agrawal R., 1998, SIGMOD Record, V27, P94, DOI 10.1145/276305.276314
[8]   Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays [J].
Alon, U ;
Barkai, N ;
Notterman, DA ;
Gish, K ;
Ybarra, S ;
Mack, D ;
Levine, AJ .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 1999, 96 (12) :6745-6750
[9]   Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm [J].
Alrosan, Ayat ;
Alomoush, Waleed ;
Alswaitti, Mohammed ;
Alissa, Khalid ;
Sahran, Shahnorbanun ;
Makhadmeh, Sharif Naser ;
Alieyan, Kamal .
CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 68 (02) :1575-1593
[10]   An LDA-Based Approach to Scientific Paper Recommendation [J].
Amami, Maha ;
Pasi, Gabriella ;
Stella, Fabio ;
Faiz, Rim .
NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS, NLDB 2016, 2016, 9612 :200-210