Consistency and Rates for Clustering with DBSCAN
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:1090-1098, 2012.
We propose a simple and efficient modification of the popular DBSCAN clustering algorithm. This modification is able to detect the most interesting vertical threshold level in an automated, data-driven way. We establish both consistency and optimal learning rates for this modification.