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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.17219 |
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| _version_ | 1866911278735294464 |
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| author | Javurek, Tomas Gregor, Michal Kula, Sebastian Simko, Marian |
| author_facet | Javurek, Tomas Gregor, Michal Kula, Sebastian Simko, Marian |
| contents | The paper introduces DelTriC (Delaunay Triangulation Clustering), a clustering algorithm which integrates PCA/UMAP-based projection, Delaunay triangulation, and a novel back-projection mechanism to form clusters in the original high-dimensional space. DelTriC decouples neighborhood construction from decision-making by first triangulating in a low-dimensional proxy to index local adjacency, and then back-projecting to the original space to perform robust edge pruning, merging, and anomaly detection. DelTriC can outperform traditional methods such as k-means, DBSCAN, and HDBSCAN in many scenarios; it is both scalable and accurate, and it also significantly improves outlier detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17219 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DelTriC: A Novel Clustering Method with Accurate Outlier Javurek, Tomas Gregor, Michal Kula, Sebastian Simko, Marian Machine Learning The paper introduces DelTriC (Delaunay Triangulation Clustering), a clustering algorithm which integrates PCA/UMAP-based projection, Delaunay triangulation, and a novel back-projection mechanism to form clusters in the original high-dimensional space. DelTriC decouples neighborhood construction from decision-making by first triangulating in a low-dimensional proxy to index local adjacency, and then back-projecting to the original space to perform robust edge pruning, merging, and anomaly detection. DelTriC can outperform traditional methods such as k-means, DBSCAN, and HDBSCAN in many scenarios; it is both scalable and accurate, and it also significantly improves outlier detection. |
| title | DelTriC: A Novel Clustering Method with Accurate Outlier |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.17219 |