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.
Original language | English (US) |
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Pages (from-to) | 1090-1098 |
Number of pages | 9 |
Journal | Journal of Machine Learning Research |
Volume | 22 |
State | Published - 2012 |
Event | 15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012 - La Palma, Spain Duration: Apr 21 2012 → Apr 23 2012 |
All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
- Control and Systems Engineering
- Statistics and Probability