Scalable Density-based Clustering with Random Projections

arXiv:2402.15679v1 Announce Type: new
Abstract: 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 …

abstract algorithm arxiv clustering clustering algorithm core cs.cv cs.lg dbscan dimensions identify property random scalable type