Abstract
The K-means algorithm is highly sensitive to the initial clustering centers and easily get trapped in a local optimum. To avoid such problems, this paper proposes an improved crossover operator of chromosomes in the genetic algorithm, redefines the calculation method of genetic probability and the natural selection rules, introduces different individual selection mechanisms for the two adjacent generations of chromosomes, and integrates the K-means algorithm into the improved genetic algorithm. Experiment results demonstrate that the improved K-Means algorithm is better than the original genetic algorithm and K-Means algorithm in clustering performance, far better than the bisecting K-means algorithm.
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Acknowledgments
The authors would like to thank the Editor and the anonymous referees for their helpful comments and suggestions to improve the quality of the paper. This research was supported in part by the Ministry of Education Humanities and Social Sciences Project (No.18YJAZH087), in part by the National Social Science Fund of China (No. 20BGL251).
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Pu, QM., Wu, Q., Li, Q. (2022). A K-means Optimized Clustering Algorithm Based on Improved Genetic Algorithm. In: Deng, Z. (eds) Proceedings of 2021 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-16-6372-7_16
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DOI: https://doi.org/10.1007/978-981-16-6372-7_16
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