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A Monte Carlo manifold spectral clustering algorithm based on emotional preference and migratory behavior

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Abstract

Inspired by various behaviors of creatures in nature, numerous efficient bionic algorithms are designed for dealing with complex clustering problems. As a population-based intelligence bionic optimization model mixes individual information interaction and physical mechanism, the emotional preference and migration behavior clustering (EPMC) algorithm had proposed for dealing with clustering tasks. It is superior to multiple classic clustering algorithms and obtains epoch-making clustering effects. However, EPMC still shows premature convergence and ineffectiveness in balancing exploration and exploitation. To further enhance the searchability and the performance during clustering, we proposed a Monte Carlo spectral clustering algorithm for emotional preference and migratory behavior optimization named MCSC-EPMC. Specifically, we first incorporate the spectral clustering strategy based on Laplacian eigenmaps to assist in updating the individual. Second, a Monte Carlo statistical data theory is utilized to simulate the cluster center point and help to approach the optimal. In addition, the theoretical analysis and convergence property of MCSC-EPMC are discussed. Numerous experiments were performed to compare the proposed MCSC-EPMC with the other seven clustering algorithms on several standard datasets. And the experimental results on several standards show the effectiveness and feasibility of MCSC-EPMC. It also enhanced the clustering performance by about 6.036% compared to the EPMC.

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Data Availability

The datasets generated and analyzed during the current study are not publicly available as they also form part of an ongoing study but are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 62276097), Key Program of National Natural Science Foundation of China (No. 62136003), National Key Research and Development Program of China (No.2020YFB1711700), Special Fund for Information Development of Shanghai Economic and Information Commission (No. XX-XXFZ-02-20-2463) and Scientific Research Program of Shanghai Science and Technology Commission (No. 21002411-000).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, East China University of Science and Technology, 200237, Shanghai, China

    Mingzhi Dai, Xiang Feng, Huiqun Yu & Weibin Guo

  2. Shanghai Engineering Research Center of Smart Energy, 200237, Shanghai, China

    Mingzhi Dai, Xiang Feng & Huiqun Yu

  3. Chinese Academy of Science and Technology for Development, 100038, Beijing, China

    Xiuquan Li

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  1. Mingzhi Dai
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Correspondence to Xiang Feng.

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Dai, M., Feng, X., Yu, H. et al. A Monte Carlo manifold spectral clustering algorithm based on emotional preference and migratory behavior. Appl Intell 53, 19742–19764 (2023). https://doi.org/10.1007/s10489-023-04484-w

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