JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (1): 65-75.doi: 10.6040/j.issn.1671-9352.0.2024.353

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Three-way K-means algorithm combining the bat algorithm and the improved compactness

SUN Qing1,2, YE Jun1,2*, ZENG Guangcai1,2, SONG Suyang1,2, WANG Yixin3   

  1. 1. Colege of lnformation Engimeering, Jiangxi University of Water Resources and Electric Power, Nanchang 330000, Jiangxi, China;
    2. Jiangxi Province Key Laboratory of Smart Water Conservancy, Nanchang 330000, Jiangxi, China;
    3. Jiangxi Open University, Nanchang 330000, Jiangxi, China
  • Published:2026-01-15

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Abstract: The three way K-means algorithm is improved by integrating the bat algorithm with closeness degree optimization. The bat algorithm is optimized by employing the golden section coefficient and population average position. The optimized bat algorithm searches for initial cluster centers which improving the stability of the three way K-means algorithm. Additionally, the threshold for core and boundary regions is determined based on closeness degree, which reduces the number of boundary samples and enhances the accuracy of the three way K-means algorithm. Comparative experiments is conducted on nine datasets against six clustering algorithms. It is shown that the proposed method improves clustering performance and is confirming its effectiveness and practical utility.

Key words: K-means clustering, bat algorithm, compactness, K-means algorithm, three way decision

CLC Number: 

  • TP391
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