Abstract
K-means is a classical clustering algorithm, which is a hard clustering algorithm. The algorithm has the advantages of simplicity and speed, especially when dealing with large data sets. It can be more efficient and flexible. However, the K-means algorithm also has some shortcomings and defects, such as relying on the initial clustering center to fall into the local optimal solution, easy to love noise points and isolated point effects. The article will optimize and demonstrate the shortcomings and defects of the K-means algorithm.
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Acknowledgments
This research is supported by the key scientific research project of Anhui Provincial Department of Education (approval number: KJ2018A0819).
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Su, Y. (2020). Improvement and Empirical Study of K-Means Clustering Algorithm Based on Chinese Retrieval. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019. ATCI 2019. Advances in Intelligent Systems and Computing, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-25128-4_24
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DOI: https://doi.org/10.1007/978-3-030-25128-4_24
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