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An Enhanced FCM Clustering Method Based on Multi-Strategy Tuna Swarm Optimization

Author

Listed:
  • Changkang Sun

    (QiLu Aerospace Information Research Institute, Jinan 250101, China
    Department of Electrical Information, Shandong University of Science and Technology, Jinan 250031, China)

  • Qinglong Shao

    (QiLu Aerospace Information Research Institute, Jinan 250101, China)

  • Ziqi Zhou

    (QiLu Aerospace Information Research Institute, Jinan 250101, China)

  • Junxiao Zhang

    (QiLu Aerospace Information Research Institute, Jinan 250101, China
    Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China)

Abstract

To overcome the shortcoming of the Fuzzy C-means algorithm (FCM)—that it is easy to fall into local optima due to the dependence of sub-spatial clustering on initialization—a Multi-Strategy Tuna Swarm Optimization-Fuzzy C-means (MSTSO-FCM) algorithm is proposed. Firstly, a chaotic local search strategy and an offset distribution estimation strategy algorithm are proposed to improve the performance, enhance the population diversity of the Tuna Swarm Optimization (TSO) algorithm, and avoid falling into local optima. Secondly, the search and development characteristics of the MSTSO algorithm are introduced into the fuzzy matrix of Fuzzy C-means (FCM), which overcomes the defects of poor global searchability and sensitive initialization. Not only has the searchability of the Multi-Strategy Tuna Swarm Optimization algorithm been employed, but the fuzzy mathematical ideas of FCM have been retained, to improve the clustering accuracy, stability, and accuracy of the FCM algorithm. Finally, two sets of artificial datasets and multiple sets of the University of California Irvine (UCI) datasets are used to do the testing, and four indicators are introduced for evaluation. The results show that the MSTSO-FCM algorithm has better convergence speed than the Tuna Swarm Optimization Fuzzy C-means (TSO-FCM) algorithm, and its accuracies in the heart, liver, and iris datasets are 89.46%, 63.58%, 98.67%, respectively, which is an outstanding improvement.

Suggested Citation

  • Changkang Sun & Qinglong Shao & Ziqi Zhou & Junxiao Zhang, 2024. "An Enhanced FCM Clustering Method Based on Multi-Strategy Tuna Swarm Optimization," Mathematics, MDPI, vol. 12(3), pages 1-16, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:453-:d:1330143
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    References listed on IDEAS

    as
    1. Ambika Aggarwal & Priti Dimri & Amit Agarwal & Madhushi Verma & Hesham A. Alhumyani & Mehedi Masud, 2021. "IFFO: An Improved Fruit Fly Optimization Algorithm for Multiple Workflow Scheduling Minimizing Cost and Makespan in Cloud Computing Environments," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, June.
    2. Jan Kubicek & Alice Varysova & Martin Cerny & Jiri Skandera & David Oczka & Martin Augustynek & Marek Penhaker, 2023. "Novel Hybrid Optimized Clustering Schemes with Genetic Algorithm and PSO for Segmentation and Classification of Articular Cartilage Loss from MR Images," Mathematics, MDPI, vol. 11(4), pages 1-26, February.
    3. MuLai Tan & YinTong Li & DaLi Ding & Rui Zhou & ChangQiang Huang & Shimin Wang, 2022. "An Improved JADE Hybridizing with Tuna Swarm Optimization for Numerical Optimization Problems," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-17, May.
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