基于德劳内三角剖分的新聚类方法，提升异常检测与聚类精度。
DelTriC: A Novel Clustering Method with Accurate Outlier
- 先在低维空间做三角剖分索引邻近关系，再回投影到原空间决策
- 在多个数据集上优于k-means、DBSCAN、HDBSCAN，且异常检测更准
- 适合高维数据聚类与异常发现，尤其对噪声敏感场景有效
本文提出DelTriC（Delaunay三角剖分聚类），一种结合主成分分析/UMAP投影、德劳内三角剖分及新型反向投影机制的聚类算法。该方法将邻域构建与聚类决策解耦：先在低维代理空间进行三角剖分以索引局部邻接关系，再回投影至原始高维空间执行鲁棒的边剪枝、合并与异常检测。DelTriC在多种场景下表现优于传统方法如k-means、DBSCAN和HDBSCAN，兼具可扩展性与高精度，并显著提升异常值检测能力。
原文摘要 · Abstract (English)
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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