GBSK: Skeleton Clustering via Granular-ball Computing and Multi-Sampling for Large-Scale Data

• URL: http://arxiv.org/abs/2509.23742v1
• Date: Sun, 28 Sep 2025 08:41:15 GMT
• Title: GBSK: Skeleton Clustering via Granular-ball Computing and Multi-Sampling for Large-Scale Data
• Authors: Yewang Chen, Junfeng Li, Shuyin Xia, Qinghong Lai, Xinbo Gao, Guoyin Wang, Dongdong Cheng, Yi Liu, Yi Wang,
• Abstract summary: We propose a novel scalable skeleton clustering algorithm, namely GBSK, to handle clustering task for large-scale datasets.<n>By multi-sampling the dataset and constructing multi-grained granular-balls, GBSK progressively uncovers a statistical "skeleton"<n>In addition, we introduce an adaptive version, AGBSK, with simplified parameter settings to enhance usability and facilitate deployment in real-world scenarios.
• Score: 62.363178614776295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To effectively handle clustering task for large-scale datasets, we propose a novel scalable skeleton clustering algorithm, namely GBSK, which leverages the granular-ball technique to capture the underlying structure of data. By multi-sampling the dataset and constructing multi-grained granular-balls, GBSK progressively uncovers a statistical "skeleton" -- a spatial abstraction that approximates the essential structure and distribution of the original data. This strategy enables GBSK to dramatically reduce computational overhead while maintaining high clustering accuracy. In addition, we introduce an adaptive version, AGBSK, with simplified parameter settings to enhance usability and facilitate deployment in real-world scenarios. Extensive experiments conducted on standard computing hardware demonstrate that GBSK achieves high efficiency and strong clustering performance on large-scale datasets, including one with up to 100 million instances across 256 dimensions. Our implementation and experimental results are available at: https://github.com/XFastDataLab/GBSK/.

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