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Adaptive K-means Algorithm Based on Three-Way Decision

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Rough Sets (IJCRS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13633))

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Abstract

The focus of traditional k-means and its related improved algorithms are to find the initial cluster centers and the appropriate number of clusters, and allocate the samples to the clusters with clear boundaries. These algorithms cannot solve the problems of clusters with imprecise boundaries and inaccurate decisions due to inaccurate information or insufficient data. Three-way clustering can solve this problem to a certain extent. However, most of the existing three-way clustering algorithms divide all clusters into three regions with the same threshold, or divide three regions subjectively. These algorithms are not suitable for clusters with different sizes and densities. To solve the above problems, an adaptive k-means algorithm based on three-way decision is proposed in this paper. First, the traditional clustering results are taken as target set and core region. The distance between each sample in the target set is used as the candidate neighborhood radius threshold. At the same time, neighborhood relationship is introduced to calculate the accuracy of approximation, upper and lower approximation of the target set under the current neighborhood relationship. Second, a boundary control coefficient is defined according to the accuracy of approximation, and as many abnormal data as possible are classified into boundary regions to transform traditional clustering into three-way clustering adapted to different sizes and densities. Finally, five indexes are compared on UCI data set and artificial data set, and the experimental results indicate the effectiveness of the proposed algorithm.

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Acknowledgments

This work was supported in part by the National Key Research and Development Program of China (No. 2020YFC2003502), the National Natural Science Foundation of China (No. 61876201), and Chongqing Talents Program (No. CQYC202210202215).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing, China

    Yihang Peng, Qinghua Zhang, Zhihua Ai & Xuechao Zhi

  2. Chongqing University of Posts and Telecommunications, Chongqing, China

    Yihang Peng, Qinghua Zhang, Zhihua Ai & Xuechao Zhi

Authors
  1. Yihang Peng
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  2. Qinghua Zhang
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  3. Zhihua Ai
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  4. Xuechao Zhi
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Corresponding author

Correspondence to Qinghua Zhang .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    JingTao Yao

  2. Iwate Prefectural University, Takizawa, Iwate, Japan

    Hamido Fujita

  3. Shanghai University, Shanghai, China

    Xiaodong Yue

  4. Tongji University, Shanghai, China

    Duoqian Miao

  5. University of Kansas, Lawrence, KS, USA

    Jerzy Grzymala-Busse

  6. Soochow University, Suzhou, Jiangsu, China

    Fanzhang Li

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Peng, Y., Zhang, Q., Ai, Z., Zhi, X. (2022). Adaptive K-means Algorithm Based on Three-Way Decision. In: Yao, J., Fujita, H., Yue, X., Miao, D., Grzymala-Busse, J., Li, F. (eds) Rough Sets. IJCRS 2022. Lecture Notes in Computer Science(), vol 13633. Springer, Cham. https://doi.org/10.1007/978-3-031-21244-4_29

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  • DOI: https://doi.org/10.1007/978-3-031-21244-4_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21243-7

  • Online ISBN: 978-3-031-21244-4

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