AISTATS 2019poster8 citations
Robustness Guarantees for Density Clustering
Heinrich Jiang, Jennifer Jang, Ofir Nachum
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.
BibTeX
@InProceedings{pmlr-v89-jiang19a,
title = {Robustness Guarantees for Density Clustering},
author = {Jiang, Heinrich and Jang, Jennifer and Nachum, Ofir},
booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
pages = {3342--3351},
year = {2019},
editor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
volume = {89},
series = {Proceedings of Machine Learning Research},
month = {16--18 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v89/jiang19a/jiang19a.pdf},
url = {https://proceedings.mlr.press/v89/jiang19a.html},
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.}
}