Robustness Guarantees for Density Clustering
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:3342-3351, 2019.
Despite the practical relevance of density-based clustering algorithms, there is little understanding in its statistical robustness properties under possibly adversarial contamination of the input data. We show both robustness and consistency guarantees for a simple modification of the popular DBSCAN algorithm. We then give experimental results which suggest that this method may be relevant in practice.