用引力协作分组提升高维数据聚类稳定性
GCAO: Group-driven Clustering via Gravitational Attraction and Optimization
- 以组为单位模拟引力吸引，替代传统点级收缩
- 在多个数据集上各项指标平均提升超37%
- 适合处理边界模糊的复杂高维数据
传统聚类算法在高维和非均匀分布数据上表现不佳，低密度边界点易受邻近簇干扰，导致结果不稳定。为此，提出基于引力吸引与优化的组驱动聚类方法（GCAO）。GCAO引入组级优化机制，将低密度边界点聚合为协同运动的组，取代传统的点级收缩过程。结合局部密度估计与邻域拓扑，构建组间有效引力交互，增强边界清晰度与结构一致性。以组为基本运动单元，采用引力收缩策略实现全局稳定且方向一致的收敛。在多个高维数据集上的实验表明，GCAO优于11种代表性聚类方法，在NMI、ARI、Homogeneity和ACC上分别取得平均37.13%、52.08%、44.98%和38.81%的提升，同时保持良好效率与可扩展性。结果凸显其在保持簇完整性、增强边界可分性和复杂数据分布下鲁棒性方面的优势。
原文摘要 · Abstract (English)
Traditional clustering algorithms often struggle with high-dimensional and non-uniformly distributed data, where low-density boundary samples are easily disturbed by neighboring clusters, leading to unstable and distorted clustering results. To address this issue, we propose a Group-driven Clustering via Gravitational Attraction and Optimization (GCAO) algorithm. GCAO introduces a group-level optimization mechanism that aggregates low-density boundary points into collaboratively moving groups, replacing the traditional point-based contraction process. By combining local density estimation with neighborhood topology, GCAO constructs effective gravitational interactions between groups and their surroundings, enhancing boundary clarity and structural consistency. Using groups as basic motion units, a gravitational contraction strategy ensures globally stable and directionally consistent convergence. Experiments on multiple high-dimensional datasets demonstrate that GCAO outperforms 11 representative clustering methods, achieving average improvements of 37.13%, 52.08%, 44.98%, and 38.81% in NMI, ARI, Homogeneity, and ACC, respectively, while maintaining competitive efficiency and scalability. These results highlight GCAO's superiority in preserving cluster integrity, enhancing boundary separability, and ensuring robust performance on complex data distributions.
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