Consistency and rates for clustering with DBSCAN

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations


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 languageEnglish (US)
Pages (from-to)1090-1098
Number of pages9
JournalJournal of Machine Learning Research
StatePublished - Jan 1 2012
Event15th International Conference on Artificial Intelligence and Statistics, AISTATS 2012 - La Palma, Spain
Duration: Apr 21 2012Apr 23 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


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