GCAO: Group-driven Clustering via Gravitational Attraction and Optimization

• URL: http://arxiv.org/abs/2510.23259v1
• Date: Mon, 27 Oct 2025 12:22:24 GMT
• Title: GCAO: Group-driven Clustering via Gravitational Attraction and Optimization
• Authors: Qi Li, Jun Wang,
• Abstract summary: We propose a Group-driven Clustering via Gravitational Attraction and Optimization (GCAO) algorithm.<n>GCAO aggregates low-density boundary points into collaboratively moving groups, replacing the traditional point-based contraction process.<n>Experiments on multiple high-dimensional datasets demonstrate that GCAO outperforms 11 representative clustering methods.
• Score: 10.85103265888665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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