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2012 AISTATS AISTATS 2012

Consistency and Rates for Clustering with DBSCAN

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.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Data Science & Analytics

🐣 Hot Topic Early Bird - learning rate

Authors

Bharath Sriperumbudur, Ingo Steinwart

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Optimization & Theory > Statistical Learning Data Science & Analytics > Applications > Clustering

Keywords

learning rate clustering algorithm density-based clustering automated threshold dbscan clustering

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