Robustness Guarantees for Density Clustering
international conference on artificial intelligence and statistics, 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 r...More
PPT (Upload PPT)