Robustness Guarantees for Density Clustering

Heinrich Jiang, Jennifer Jang, Ofir Nachum
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:3342-3351, 2019.

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 this Paper

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.} }
Endnote
%0 Conference Paper %T Robustness Guarantees for Density Clustering %A Heinrich Jiang %A Jennifer Jang %A Ofir Nachum %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-jiang19a %I PMLR %P 3342--3351 %U https://proceedings.mlr.press/v89/jiang19a.html %V 89 %X 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.
APA
Jiang, H., Jang, J. & Nachum, O.. (2019). Robustness Guarantees for Density Clustering. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:3342-3351 Available from https://proceedings.mlr.press/v89/jiang19a.html.

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