Abstract
Clustering is an unsupervised machine learning technique for data mining to find objects with similar characteristics in a group. However, due to the lack of relevant prior information on the data, numerous single models or methods cannot identify the shape and size of the clusters. Therefore, an ensemble of multiple weak models is required to further mine the implicit information of the data and improve the clustering accuracy. LSMC-EPMC is an evolutionary clustering algorithm that consists of three parts, the emotional preference and migration behavior clustering (EPMC) model, the Laplacian spectral clustering model, and the Monte Carlo statistical data simulation model. This paper mainly integrates the spectral clustering model and the Monte Carlo statistical data simulation method into the EPMC algorithm by mapping the individual in EPMC and the optimized center point in the other two methods. Through numerous experiments, LSMC-EPMC shows a significantly increased performance to EPMC and is highly competitive with the other seven clustering algorithms on several standard datasets.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant NOs. 61772200, Shanghai Pujiang Talent Program (17PJ1401900), the Information Development Special Funds of Shanghai Economic and Information Commission under Grant NO. XX-XXFZ-02-20-2463, and the Key Program of National Natural Science Foundation of China (62136003).
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Dai, M., Feng, X., Yu, H., Guo, W. (2022). A Novel Spectral Ensemble Clustering Algorithm Based on Social Group Migratory Behavior and Emotional Preference. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_25
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