Almost Linear Time Consistent Mode Estimation and Quick Shift Clustering
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 11 Mar 2025.
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References · 6
- 2024The Faiss librarycited by 376
- 2018Hashing-Based-Estimators for Kernel Density in High Dimensionscited by 33
- 2020Kernel Density Estimation through Density Constrained Near Neighbor Searchcited by 29
- 2018DBSCAN++: Towards fast and scalable density clusteringcited by 20
- 2021MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Trackingcited by 9
- 2017On the Consistency of Quick Shiftcited by 7
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