Gradient-Guided Density Peak Clustering

arXiv:2610.01050 · math.ST, stat.ME, stat.ML, stat.TH · Submitted 2026-10-01 · Read on arXiv

math.ST, stat.ME, stat.ML, stat.TH

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/zhangyk8/GGDPC

Terminology

• Clustering — 84× in this paper · explained in 2 episodes
• Consistency — 26× in this paper · explained in 3 episodes
• Gradient Flow — 25× in this paper · explained in 2 episodes
• Core — 10× in this paper · explained in 2 episodes
• Gradient-Guided Density Peak Clustering — 7× in this paper
• Adjusted Rand Index — 4× in this paper
• Degeneracy — 4× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Ambient Dimension — 1× in this paper · explained in 1 episode
• Quantization — 1× in this paper · explained in 12 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• A review of mean-shift algorithms for clustering
• Clusters and water flows: a novel approach to modal clustering through Morse theory

Related papers

• Conformal Prediction for Dyadic Regression Under Complex Missingness
• Bentkus-type asymptotic e-values
• High-Dimensional Asymptotics of Differentially Private PCA
• KL Convergence Guarantees for Score diffusion models under minimal data assumptions
• Geometric bias in eigenspace perturbation under random heterogeneous noise
• On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models