Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering
Machine Learning
2025-12-01 v2 Machine Learning
Abstract
In this paper, we propose a method for density-based clustering in high-dimensional spaces that combines Locality-Sensitive Hashing (LSH) with the Quick Shift algorithm. The Quick Shift algorithm, known for its hierarchical clustering capabilities, is extended by integrating approximate Kernel Density Estimation (KDE) using LSH to provide efficient density estimates. The proposed approach achieves almost linear time complexity while preserving the consistency of density-based clustering.
Keywords
Cite
@article{arxiv.2503.07995,
title = {Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering},
author = {Sajjad Hashemian},
journal= {arXiv preprint arXiv:2503.07995},
year = {2025}
}
Comments
15 pages, 3 figures, Accepted to OLA 2025