  DBSCAN is widely used in many scientific and engineering fields because of
its simplicity and practicality. However, due to its high sensitivity
parameters, the accuracy of the clustering result depends heavily on practical
experience. In this paper, we first propose a novel Deep Reinforcement Learning
guided automatic DBSCAN parameters search framework, namely DRL-DBSCAN. The
framework models the process of adjusting the parameter search direction by
perceiving the clustering environment as a Markov decision process, which aims
to find the best clustering parameters without manual assistance. DRL-DBSCAN
learns the optimal clustering parameter search policy for different feature
distributions via interacting with the clusters, using a weakly-supervised
reward training policy network. In addition, we also present a recursive search
mechanism driven by the scale of the data to efficiently and controllably
process large parameter spaces. Extensive experiments are conducted on five
artificial and real-world datasets based on the proposed four working modes.
The results of offline and online tasks show that the DRL-DBSCAN not only
consistently improves DBSCAN clustering accuracy by up to 26% and 25%
respectively, but also can stably find the dominant parameters with high
computational efficiency. The code is available at
https://github.com/RingBDStack/DRL-DBSCAN.
