用图传播方法实现高维数据的变密度聚类，速度快且精度高。
Scalable Varied-Density Clustering via Graph Propagation
- 将聚类视为自适应局部密度的图标签传播过程
- 百万级数据点可在分钟内完成，精度媲美现有方法
- 适合大规模高维数据聚类，尤其关注效率与可扩展性
我们提出一种新的变密度聚类视角，将高维数据的聚类问题建模为在随局部密度变化自适应的邻域图中进行标签传播。该方法形式化连接了基于密度的聚类与图连通性，使网络科学中的高效图传播技术得以应用。为保证可扩展性，我们设计了一种密度感知的邻域传播算法，并利用先进的随机投影方法构建近似邻域图。该方法显著降低计算开销，同时保持聚类质量。实验表明，该方法可在几分钟内处理百万级数据点，且精度与现有基线相当。
原文摘要 · Abstract (English)
We propose a novel perspective on varied-density clustering for high-dimensional data by framing it as a label propagation process in neighborhood graphs that adapt to local density variations. Our method formally connects density-based clustering with graph connectivity, enabling the use of efficient graph propagation techniques developed in network science. To ensure scalability, we introduce a density-aware neighborhood propagation algorithm and leverage advanced random projection methods to construct approximate neighborhood graphs. Our approach significantly reduces computational cost while preserving clustering quality. Empirically, it scales to datasets with millions of points in minutes and achieves competitive accuracy compared to existing baselines.
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