2024/02/24 by Haochuan Xu, Xu, Haochuan, Ninh Pham +1
Computer Science · #Advanced Clustering Algorithms Research #Bayesian Methods and Mixture Models #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2402.15679
openalex publication_date 2024/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present sDBSCAN, a scalable density-based clustering algorithm in high dimensions with cosine distance. Utilizing the neighborhood-preserving property of random projections, sDBSCAN can quickly identify core points and their neighborhoods, the primary hurdle of density-based clustering. Theoretically, sDBSCAN outputs a clustering structure similar to DBSCAN under mild conditions with high probability. To further facilitate sDBSCAN, we present sOPTICS, a scalable OPTICS for interactive exploration of the intrinsic clustering structure. We also extend sDBSCAN and sOPTICS to L2, L1, χ2, and Jensen-Shannon distances via random kernel features. Empirically, sDBSCAN is significantly faster and provides higher accuracy than many other clustering algorithms on real-world million-point data sets. On these data sets, sDBSCAN and sOPTICS run in a few minutes, while the scikit-learn's counterparts demand several hours or cannot run due to memory constraints.