Robustness Guarantees for Density Clustering

international conference on artificial intelligence and statistics, 2019.

Cited by: 2|Bibtex|Views6
EI
Other Links: academic.microsoft.com|dblp.uni-trier.de

Abstract:

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

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