Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering
This addresses the computational bottleneck in high-dimensional clustering for data analysis applications, representing an incremental improvement.
The paper tackles efficient density-based clustering in high-dimensional spaces by combining Locality-Sensitive Hashing with the Quick Shift algorithm, achieving almost linear time complexity while maintaining clustering consistency.
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.