Scalable DBSCAN with Random Projections

HaoChuan Xu, Ninh Pham

Keywords: Density-based clustering, random projections, extreme order statistics

TL;DR: Scale up DBSCAN and OPTICS with random projections

Abstract:

We present sDBSCAN, a scalable density-based clustering algorithm in high dimensions with cosine distance. sDBSCAN leverages recent advancements in random projections given a significantly large number of random vectors to quickly identify core points and their neighborhoods, the primary hurdle of density-based clustering. Theoretically, sDBSCAN preserves the DBSCAN’s clustering structure under mild conditions with high probability. To facilitate sDBSCAN, we present sOPTICS, a scalable visual tool to guide the parameter setting of sDBSCAN. 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 competitive DBSCAN variants on real-world million-point data sets. On these data sets, sDBSCAN and sOPTICS run in a few minutes, while the scikit-learn counterparts and other clustering competitors demand several hours or
cannot run on our hardware due to memory constraints. Our code is available at https://github.com/NinhPham/sDbscan.

Primary Area: Evaluation (methodology, meta studies, replicability and validity)

Submission Number: 3911