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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2605.00390 |
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| _version_ | 1866914524189163520 |
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| author | Zheng, Yingtao Phibbs, Hugo Pham, Ninh |
| author_facet | Zheng, Yingtao Phibbs, Hugo Pham, Ninh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00390 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Robust and Scalable Density-based Clustering via Graph Propagation Zheng, Yingtao Phibbs, Hugo Pham, Ninh Machine Learning 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. |
| title | Towards Robust and Scalable Density-based Clustering via Graph Propagation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.00390 |