Scalable Varied-Density Clustering via Graph Propagation
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912519853965312 |
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| author | Pham, Ninh Zheng, Yingtao Phibbs, Hugo |
| author_facet | Pham, Ninh Zheng, Yingtao Phibbs, Hugo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Varied-Density Clustering via Graph Propagation Pham, Ninh Zheng, Yingtao Phibbs, Hugo Machine Learning 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. |
| title | Scalable Varied-Density Clustering via Graph Propagation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.02989 |