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Clustering Based on Continuous Hopfield Network

Author

Listed:
  • Yao Xiao

    (Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing 404188, China)

  • Yashu Zhang

    (Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing 404188, China)

  • Xiangguang Dai

    (Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing 404188, China)

  • Dongfang Yan

    (Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing 404188, China)

Abstract

Clustering aims to group n data samples into k clusters. In this paper, we reformulate the clustering problem into an integer optimization problem and propose a recurrent neural network with n × k neurons to solve it. We prove the stability and convergence of the proposed recurrent neural network theoretically. Moreover, clustering experiments demonstrate that the proposed clustering algorithm based on the recurrent neural network can achieve the better clustering performance than existing clustering algorithms.

Suggested Citation

  • Yao Xiao & Yashu Zhang & Xiangguang Dai & Dongfang Yan, 2022. "Clustering Based on Continuous Hopfield Network," Mathematics, MDPI, vol. 10(6), pages 1-11, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:944-:d:771963
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