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A migratory behavior and emotional preference clustering algorithm based on learning vector quantization and gaussian mixture model

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Abstract

Clustering based on swarm intelligence optimization plays a central role in engineering and mathematics. The ordinary emotional preference migration model has failed to achieve a good cluster effect and easily falls into the optimal local solution. Therefore, in order to obtain a higher quality of clustering and a more precise number of clusters, the paper focuses on the integral increment of the particular learning vector quantization and the Gaussian mixture method. By combining the label information and the distribution information of the dataset to facilitate clustering, we have proposed a new algorithm named GLEPMC. In this way, the supervised and unsupervised information of the data can be fully utilized for clustering. Through numerous experiments, the performance of the proposed GLEPMC algorithm is better than the ordinary emotional preference migration model and the other six algorithms. Theoretical analyses also prove the convergence of our proposed GLEPMC algorithm.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant NOs. 61772200, 61772201 and 61602175, Shanghai Pujiang Talent Program (17PJ1401900), the Information Development Special Funds of Shanghai Economic and Information Commission under Grant NO. 201602008, and National Natural Science Foundation of China under Grant NO. 62136003.

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Authors and Affiliations

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

    Mingzhi Dai, Xiang Feng, Huiqun Yu & Weibin Guo

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

    Mingzhi Dai, Xiang Feng & Huiqun Yu

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  1. Mingzhi Dai
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  2. Xiang Feng
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  3. Huiqun Yu
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Correspondence to Xiang Feng.

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Dai, M., Feng, X., Yu, H. et al. A migratory behavior and emotional preference clustering algorithm based on learning vector quantization and gaussian mixture model. Appl Intell 52, 17185–17216 (2022). https://doi.org/10.1007/s10489-022-03325-6

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