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）与快速移位算法相结合。快速移位算法以其层次聚类能力而闻名，本文通过整合基于LSH的近似核密度估计（KDE）来提供高效的密度估计，从而对该算法进行了扩展。所提出的方法在保持基于密度聚类一致性的同时，实现了近乎线性的时间复杂度。