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.

翻译：本文提出了一种在高维空间中基于密度的聚类方法，该方法将局部敏感哈希（LSH）与快速移位（Quick Shift）算法相结合。快速移位算法以其层次聚类能力而闻名，通过集成基于LSH的近似核密度估计（KDE）以提供高效的密度估计，从而得到扩展。所提出的方法在保持基于密度聚类一致性的同时，实现了近似线性的时间复杂度。