Towards Robust and Scalable Density-based Clustering via Graph Propagation

Yingtao Zheng; Hugo Phibbs; Ninh Pham

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arXiv · 2605.00390

Towards Robust and Scalable Density-based Clustering via Graph Propagation

Yingtao Zheng·Hugo Phibbs·Ninh Pham

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Abstract

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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Yingtao Zheng, Hugo Phibbs, Ninh Pham. 2026-05-01. Towards Robust and Scalable Density-based Clustering via Graph Propagation. https://arxiv.org/abs/2605.00390

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