用图传播重构密度聚类，让高维数据聚类更快更准。
Towards Robust and Scalable Density-based Clustering via Graph Propagation
- 将聚类转为图上的标签传播，自动处理不同密度区域。
- 可处理百万级数据点，分钟内完成，精度超越现有方法。
- 不依赖距离度量，适合大规模真实数据场景。
我们提出一种名为 CluProp 的新框架，将高维空间中的多密度聚类重新构想为邻域图上的标签传播过程。该方法在密度聚类与图连通性之间建立正式关联，利用网络科学中的高效传播机制，缓解传统密度聚类对参数的敏感性。具体而言，引入确定性的密度驱动传播策略，实现可扩展的邻域识别。该框架对距离度量选择无感，在大规模数据上表现优异，可在几分钟内处理数百万个点，并在准确率上持续优于现有基线方法。
原文摘要 · Abstract (English)
We present \textit{CluProp}, a novel framework that reimagines varied-density clustering in high-dimensional spaces as a label propagation process over neighborhood graphs. Our approach formally bridges the gap between density-based clustering and graph connectivity, leveraging efficient propagation mechanisms from network science to mitigate the parameter sensitivity inherent in traditional density-based methods. Specifically, we introduce a deterministic density-based propagation strategy to ensure scalable neighborhood identification. The framework is agnostic to the choice of distance metric and exhibits superior performance on large-scale data, processing millions of points in minutes while consistently outperforming existing baselines in accuracy.
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