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May 8, 2026Scientific ReportsOpen Access

Multi-granularity collaborative clustering based on adaptive granular-balls

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Authors

XZXingguo ZhangLXLi XUWJWeikuan Jia

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Overview

Randomized trial demonstrates improved clustering outcomes in various datasets, suggesting enhanced adaptability and efficiency.

Key Points

  • The aim is to improve clustering methods using adaptive granular-balls to handle noise and boundary issues in datasets.
  • Introduced a nearest neighbor method for adaptive granular-ball generation.
  • Divided granular-balls into high-compactness and low-compactness subsets based on compactness.
  • Employed an intersection-based principle for clustering high-compactness granular-balls and a shortest-distance criterion for low-compactness.
  • AGB-MCC outperformed existing granular-ball methods in dealing with noise points and overlapping clusters.
  • Showed strong adaptability across diverse datasets with high clustering robustness.
  • Achieved improved computational efficiency compared to traditional clustering methods.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69fd7d4abfa21ec5bbf05e39https://doi.org/10.1038/s41598-026-50637-5
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