Code for 'Automating DBSCAN via Reinforcement Learning' accepted by CIKM2022

• main.py: getting started.
• model/model.py: overall framework of DRL-DBSCAN.
• model/environment.py: environment of the reinforcement learning process.
• model/TD3.py: TD3 structure.
• utils/utils.py: utils for data preprocessing, loading and other functions.
• utils/plot.py: visual functions

the experimental dataset consists of 4 artificial clustering benchmark datasets and 1 public real-world streaming dataset. The benchmark datasets are the 2D shape sets, including: Aggregation, Compound, Pathbased, and D31. They involve multiple density types such as clusters within clusters, multi-density, multi-shape, closed loops, etc., and have various data scales. Furthermore, the real-world streaming dataset Sensor comes from consecutive information (temperature, humidity, light, and sensor voltage) collected from 54 sensors deployed by the Intel Berkeley Research Lab. We use a subset of 80, 864 for experiments and divide these objects into 16 data blocks (V1, ..., V16) as an online dataset.

We compare proposed DRL-DBSCAN with three types of baselines: (1) traditional hyperparameter search CIKM’22, October 17-22, 2022, Hybrid Conference, Hosted in Atlanta, Georgia, USA Ruitong Zhang, et al. schemes: random search algorithm Rand, Bayesian optimization based on Tree-structured Parzen estimator algorithm BO-TPE; (2) meta-heuristic optimization algorithms: the simulated annealing optimization Anneal, particle swarm optimization PSO, genetic algorithm GA, and differential evolution algorithm DE; (3) existing DBSCAN parameter search methods: KDist (V-DBSCAN) and BDE-DBSCAN

If you find this repository helpful, please consider citing the following paper.