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Improved multi-objective clustering algorithm using particle swarm optimization

Author

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  • Congcong Gong
  • Haisong Chen
  • Weixiong He
  • Zhanliang Zhang

Abstract

Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0188815
    DOI: 10.1371/journal.pone.0188815
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    References listed on IDEAS

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    1. 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.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
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    Cited by:

    1. Amit Rathee & Jitender Kumar Chhabra, 2020. "Mining Reusable Software Components from Object-Oriented Source Code using Discrete PSO and Modeling Them as Java Beans," Information Systems Frontiers, Springer, vol. 22(6), pages 1519-1537, December.
    2. Bae, Hyeong-Ohk & Ha, Seung-Yeal & Kang, Myeongju & Lim, Hyuncheul & Min, Chanho & Yoo, Jane, 2022. "A constrained consensus based optimization algorithm and its application to finance," Applied Mathematics and Computation, Elsevier, vol. 416(C).
    3. Yukun Dong & Yu Zhang & Fubin Liu & Zhengjun Zhu, 2022. "Research on an Optimization Method for Injection-Production Parameters Based on an Improved Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 15(8), pages 1-18, April.

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