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An improved preconditioned unsupervised K-means clustering algorithm

Author

Listed:
  • Tiantian Sun

    (South China Normal University
    Xiangtan University)

  • Xiaofei Peng

    (South China Normal University)

  • Wenxiu Ge

    (South China Normal University)

  • Weiwei Xu

    (Nanjing University of Information Science and Technology)

Abstract

The Unsupervised K-means clustering (UKM) algorithm has attracted the attention of many researchers because it can automatically identify the number of clusters without requiring any parameter selection. However, it may produce poor clustering results on datasets with Gaussian mixtures. In this paper, we consider the preconditioned UKM algorithm, where the truncated UKM algorithm is first used as a preconditioning strategy. To further enhance the algorithm’s performance, we introduce a circular modification strategy. In particular, we determine whether to use the above strategies based on the Bayesian Information Criterion (BIC). The experimental results reveal that the proposed algorithms have a higher clustering accuracy than the UKM algorithm when applied to Gaussian mixture datasets.

Suggested Citation

  • Tiantian Sun & Xiaofei Peng & Wenxiu Ge & Weiwei Xu, 2025. "An improved preconditioned unsupervised K-means clustering algorithm," Computational Statistics, Springer, vol. 40(8), pages 4187-4207, November.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:8:d:10.1007_s00180-025-01616-3
    DOI: 10.1007/s00180-025-01616-3
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    References listed on IDEAS

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    1. Lingsong Meng & Dorina Avram & George Tseng & Zhiguang Huo, 2022. "Outcome‐guided sparse K‐means for disease subtype discovery via integrating phenotypic data with high‐dimensional transcriptomic data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 352-375, March.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Xiaoyun Tian & Dachuan Xu & Donglei Du & Ling Gai, 2022. "The spherical k-means++ algorithm via local search scheme," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2375-2394, November.
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