Automatic depth-based local center clustering via $β$-integrated local depth and adaptive grouping
Clustering is an unsupervised learning technique that partitions unlabeled data into groups. Most existing methods require user-specified parameters, such as the number of clusters or neighborhood size. Conversely, we propose automatic depth-based local center clustering (A-DLCC), a fully data-driven method that eliminates numerical parameter tuning. A-DLCC uses the $β$-integrated local depth to identify stable exemplars, points consistently central across multiple locality levels, termed local centers, which are ranked by their representativeness. Each local center induces a group of similar points, with group-level similarity measured by a proposed nonparametric metric called group-leve...
| Authors | Siyi Wang, Alexandre Leblanc, Paul D. McNicholas |
|---|---|
| Primary category | stat.ME |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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