Abstract
In density peak clustering the choice of cut-off distance is not theoretically supported, and to address this concern, we propose density clustering based on firefly algorithm. The certainty between data is determined on the basis of density estimation entropy. The cut-off distance corresponding to the minimum entropy is found by iterative optimization of FA, and then substituted into the standard density clustering algorithm. Simulation experiments are conducted on eight artificial datasets. Compared with the standard density peak clustering, our method can choose the cut-off distance in a self-adaptive manner on different datasets, which improves the clustering effect.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China under Grant (Nos. 61663029, 51669014, 61563036), The Science Fund for Distinguished Young Scholars of Jiangxi Province under Grant (No. 2018ACB21029), National Undergraduate Training Programs for Innovation and Entrepreneurship under Grant (No. 201711319001) and the project of Nanchang Institute of Technology’s graduate student innovation program under Grant (Nos. YJSCX2017023, YJSCX20180023).
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Wang, J., Fan, T., Xie, Z., Zhang, X., Zhao, J. (2019). Density Peak Clustering Based on Firefly Algorithm. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_2
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DOI: https://doi.org/10.1007/978-981-13-5841-8_2
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