Automatic depth-based local center clustering via beta-integrated local depth and adaptive grouping

arXiv:2609.26748 · stat.ME, cs.LG, stat.ML · Submitted 2026-09-22 · Read on arXiv

stat.ME, cs.LG, stat.ML

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/lytgysrn/ADLCC-python

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Clustering — 98× in this paper · explained in 2 episodes
• Spectral clustering — 7× in this paper · explained in 2 episodes
• Objective Function — 6× in this paper · explained in 2 episodes
• Modularity — 4× in this paper · explained in 9 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Representation Learning — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Breast Cancer — 1× in this paper
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes

Sources

• K*-Means: A Parameter-free Clustering Algorithm
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Depth-Based Local Center Clustering: A Framework for Handling Different Clustering Scenarios
• $\beta$-integrated local depth and corresponding partitioned local depth representation

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