用合成数据训练GNN，精准识别高维噪声中的聚类倾向
ACTGNN: Assessment of Clustering Tendency with Synthetically-Trained Graph Neural Networks
- 用LSH和RBF核构建图特征，通过GNN学习聚类结构
- 在高维/噪声数据上超越传统方法，能发现微弱聚类模式
- 适合需要可靠聚类评估的科研与工程场景
确定数据集的聚类倾向是基础但具挑战性的任务，尤其在噪声或高维环境下，传统方法如霍普金斯统计量和视觉聚类倾向评估（VAT）常难以得出可靠结果。本文提出ACTGNN，一种基于图神经网络的聚类倾向评估框架。节点特征通过局部敏感哈希（LSH）构建，捕捉局部邻域信息；边特征融合多种相似性度量，如径向基函数（RBF）核，以建模成对关系。该GNN仅在合成数据上训练，从而在受控条件下学习鲁棒的聚类结构。大量实验表明，ACTGNN在合成与真实数据集上均显著优于基线方法，尤其在高维或噪声数据中仍能有效检测微弱聚类结构。结果验证了该方法的泛化能力与有效性，为鲁棒聚类倾向评估提供了有力工具。
原文摘要 · Abstract (English)
Determining clustering tendency in datasets is a fundamental but challenging task, especially in noisy or high-dimensional settings where traditional methods, such as the Hopkins Statistic and Visual Assessment of Tendency (VAT), often struggle to produce reliable results. In this paper, we propose ACTGNN, a graph-based framework designed to assess clustering tendency by leveraging graph representations of data. Node features are constructed using Locality-Sensitive Hashing (LSH), which captures local neighborhood information, while edge features incorporate multiple similarity metrics, such as the Radial Basis Function (RBF) kernel, to model pairwise relationships. A Graph Neural Network (GNN) is trained exclusively on synthetic datasets, enabling robust learning of clustering structures under controlled conditions. Extensive experiments demonstrate that ACTGNN significantly outperforms baseline methods on both synthetic and real-world datasets, exhibiting superior performance in detecting faint clustering structures, even in high-dimensional or noisy data. Our results highlight the generalizability and effectiveness of the proposed approach, making it a promising tool for robust clustering tendency assessment.
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