Consistency and Rates for Clustering with DBSCAN

Bharath Sriperumbudur, Ingo Steinwart
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:1090-1098, 2012.

Abstract

We propose a simple and efficient modification of the popular DBSCAN clustering algorithm. This modification is able to detect the most interesting vertical threshold level in an automated, data-driven way. We establish both consistency and optimal learning rates for this modification.

Cite this Paper

BibTeX
@InProceedings{pmlr-v22-sriperumbudur12, title = {Consistency and Rates for Clustering with DBSCAN}, author = {Sriperumbudur, Bharath and Steinwart, Ingo}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {1090--1098}, year = {2012}, editor = {Lawrence, Neil D. and Girolami, Mark}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/sriperumbudur12/sriperumbudur12.pdf}, url = {https://proceedings.mlr.press/v22/sriperumbudur12.html}, abstract = {We propose a simple and efficient modification of the popular DBSCAN clustering algorithm. This modification is able to detect the most interesting vertical threshold level in an automated, data-driven way. We establish both consistency and optimal learning rates for this modification.} }
Endnote
%0 Conference Paper %T Consistency and Rates for Clustering with DBSCAN %A Bharath Sriperumbudur %A Ingo Steinwart %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-sriperumbudur12 %I PMLR %P 1090--1098 %U https://proceedings.mlr.press/v22/sriperumbudur12.html %V 22 %X We propose a simple and efficient modification of the popular DBSCAN clustering algorithm. This modification is able to detect the most interesting vertical threshold level in an automated, data-driven way. We establish both consistency and optimal learning rates for this modification.
RIS
TY - CPAPER TI - Consistency and Rates for Clustering with DBSCAN AU - Bharath Sriperumbudur AU - Ingo Steinwart BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-sriperumbudur12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 22 SP - 1090 EP - 1098 L1 - http://proceedings.mlr.press/v22/sriperumbudur12/sriperumbudur12.pdf UR - https://proceedings.mlr.press/v22/sriperumbudur12.html AB - We propose a simple and efficient modification of the popular DBSCAN clustering algorithm. This modification is able to detect the most interesting vertical threshold level in an automated, data-driven way. We establish both consistency and optimal learning rates for this modification. ER -
APA
Sriperumbudur, B. & Steinwart, I.. (2012). Consistency and Rates for Clustering with DBSCAN. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 22:1090-1098 Available from https://proceedings.mlr.press/v22/sriperumbudur12.html.

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