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Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing

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  • Ahmad Abubaker
  • Adam Baharum
  • Mahmoud Alrefaei

Abstract

This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets.

Suggested Citation

  • Ahmad Abubaker & Adam Baharum & Mahmoud Alrefaei, 2015. "Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-23, July.
    • Handle: RePEc:plo:pone00:0130995
    DOI: 10.1371/journal.pone.0130995
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    References listed on IDEAS

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    1. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
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    1. Clarisse Dhaenens & Laetitia Jourdan, 2022. "Metaheuristics for data mining: survey and opportunities for big data," Annals of Operations Research, Springer, vol. 314(1), pages 117-140, July.
    2. Clarisse Dhaenens & Laetitia Jourdan, 2019. "Metaheuristics for data mining," 4OR, Springer, vol. 17(2), pages 115-139, June.
    3. Congcong Gong & Haisong Chen & Weixiong He & Zhanliang Zhang, 2017. "Improved multi-objective clustering algorithm using particle swarm optimization," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-19, December.
    4. Alokananda Dey & Siddhartha Bhattacharyya & Sandip Dey & Debanjan Konar & Jan Platos & Vaclav Snasel & Leo Mrsic & Pankaj Pal, 2023. "A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering," Mathematics, MDPI, vol. 11(9), pages 1-44, April.

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