Towards Robust and Scalable Density-based Clustering via Graph Propagation

TL;DR

CluProp is a new density-based clustering framework that uses graph propagation to improve scalability and accuracy in high-dimensional data, reducing parameter sensitivity.

Contribution

It introduces a deterministic propagation strategy that bridges density-based clustering with graph connectivity, enabling scalable and metric-agnostic clustering.

Findings

Processes millions of points in minutes.

Outperforms existing baselines in accuracy.

Mitigates parameter sensitivity in clustering.

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.