Scalable Varied-Density Clustering via Graph Propagation

Ninh Pham; Yingtao Zheng; Hugo Phibbs

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arXiv · 2508.02989

Scalable Varied-Density Clustering via Graph Propagation

Ninh Pham·Yingtao Zheng·Hugo Phibbs

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Abstract

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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BibTeX RIS

Ninh Pham, Yingtao Zheng, Hugo Phibbs. 2025-08-05. Scalable Varied-Density Clustering via Graph Propagation. https://arxiv.org/abs/2508.02989

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