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September 20, 2025

Granular-Ball-Induced Multiple Kernel K-Means

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Authors

SXShuyin XiaYWYifan WangLSLifeng Shen

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Overview

Granular-ball computing enhances clustering efficiency and robustness in high-dimensional data, suggesting improvements in multi-kernel algorithms.

Key Points

  • The proposed granular-ball multi-kernel K-means framework improves clustering performance in complex data.
  • This technique enhances computational efficiency, addressing challenges faced by traditional multi-kernel K-means methods.
  • Granular-ball relationships enable better adaptation of data distribution, improving robustness against unknown noises.
  • Empirical evaluations across various tasks demonstrate significant advantages of the granular-ball framework.

Cite This Study

Xia et al. (2025) studied this question.

synapsesocial.com/papers/68d469d631b076d99fa66eebhttps://doi.org/10.24963/ijcai.2025/738
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