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.
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Keyword Pioneer
— density-based clustering
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy
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Interdisciplinary Bridge
— Data Science & Analytics and Machine Learning
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Hot Topic Early Bird
— learning rate