Robustness Guarantees for Density Clustering
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 results which suggest that this method may be relevant in practice.
Cite
Text
Jiang et al. "Robustness Guarantees for Density Clustering." Artificial Intelligence and Statistics, 2019.Markdown
[Jiang et al. "Robustness Guarantees for Density Clustering." Artificial Intelligence and Statistics, 2019.](https://mlanthology.org/aistats/2019/jiang2019aistats-robustness/)BibTeX
@inproceedings{jiang2019aistats-robustness,
title = {{Robustness Guarantees for Density Clustering}},
author = {Jiang, Heinrich and Jang, Jennifer and Nachum, Ofir},
booktitle = {Artificial Intelligence and Statistics},
year = {2019},
pages = {3342-3351},
volume = {89},
url = {https://mlanthology.org/aistats/2019/jiang2019aistats-robustness/}
}