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.

🧭 Keyword Pioneer — density-based clustering
🐝 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
🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🐣 Hot Topic Early Bird — learning rate