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arXiv · 2511.17219

DelTriC: A Novel Clustering Method with Accurate Outlier

Tomas Javurek·Michal Gregor·Sebastian Kula·Marian Simko
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Abstract

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.

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Tomas Javurek, Michal Gregor, Sebastian Kula, Marian Simko. 2025-11-21. DelTriC: A Novel Clustering Method with Accurate Outlier. https://arxiv.org/abs/2511.17219

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