arXiv 2509.237422025-09-28cs.LGcs.CVcs.IR

GBSK：面向大规模数据的基于粒球计算与多重采样的骨架聚类

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

College of Computer Science and Technology, Huaqiao University（华侨大学计算机科学与技术学院）；Fujian Provincial Key Laboratory of Computer Vision and Machine Learning, Huaqiao University（华侨大学计算机视觉与机器学习省级重点实验室）；Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education（教育部网络空间大数据安全重点实验室）；Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications（重庆邮电大学计算智能市级重点实验室）；Chongqing Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications（重庆邮电大学大数据智能计算市级重点实验室）；College of Computer and Information Science, Chongqing Normal University（重庆师范大学计算机与信息科学学院）；College of Big Data and Intelligent Engine（大数据与智能工程学院）

Yewang Chen, Junfeng Li, Shuyin Xia, Qinghong Lai, Xinbo Gao, Guoyin Wang, Dongdong Cheng, Yi Liu, Yi Wang

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AI总结：

本文提出可扩展骨架聚类算法GBSK，利用多重采样与多粒度粒球提取数据统计骨架，并给出自适应AGBSK，在亿级256维数据上兼顾效率与聚类精度。

摘要：

为有效处理大规模数据集的聚类任务，我们提出一种新颖的可扩展骨架聚类算法，即GBSK，该算法利用粒球技术捕捉数据的潜在结构。通过对数据集进行多重采样并构建多粒度粒球，GBSK逐步揭示出一个统计“骨架”——一种近似原始数据本质结构与分布的空间抽象。该策略使GBSK能够在保持高聚类准确率的同时，大幅降低计算开销。此外，我们提出了一个自适应版本AGBSK，其具有简化的参数设置，以增强可用性并促进在真实场景中的部署。在标准计算硬件上开展的大量实验表明，GBSK在大规模数据集上实现了高效率和强大的聚类性能，其中包括一个包含多达1亿个实例、覆盖256个维度的数据集。我们的实现和实验结果可在 https://github.com/XFastDataLab/GBSK/ 获取。

英文摘要：

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/.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。