用分粒球和多采样法，快速处理上亿数据的聚类。
GBSK: Skeleton Clustering via Granular-ball Computing and Multi-Sampling for Large-Scale Data
- 通过多尺度分粒球捕捉数据结构，逐步构建统计骨架。
- 在256维、1亿样本数据上保持高精度，计算开销大幅降低。
- 自适应版本参数少，适合实际部署，开源可复现。
为高效处理大规模数据聚类任务，我们提出一种新型可扩展骨架聚类算法GBSK，利用分粒球技术捕捉数据潜在结构。通过多采样数据集并构建多粒度分粒球，GBSK逐步揭示一个统计‘骨架’——即原数据本质结构与分布的空间抽象。该策略显著降低计算开销，同时保持高聚类精度。此外，我们设计了参数简化版自适应算法AGBSK，提升易用性，便于在真实场景中部署。在标准硬件上进行的大量实验表明，GBSK在大规模数据集上表现优异，包括一个包含1亿实例、维度达256的数据集。代码与实验结果已公开于：https://github.com/XFastDataLab/GBSK/。
原文摘要 · Abstract (English)
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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