Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning (2505.04339v1)

Abstract: DBSCAN, a well-known density-based clustering algorithm, has gained widespread popularity and usage due to its effectiveness in identifying clusters of arbitrary shapes and handling noisy data. However, it encounters challenges in producing satisfactory cluster results when confronted with datasets of varying density scales, a common scenario in real-world applications. In this paper, we propose a novel Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning cluster framework, namely AR-DBSCAN. First, we model the initial dataset as a two-level encoding tree and categorize the data vertices into distinct density partitions according to the information uncertainty determined in the encoding tree. Each partition is then assigned to an agent to find the best clustering parameters without manual assistance. The allocation is density-adaptive, enabling AR-DBSCAN to effectively handle diverse density distributions within the dataset by utilizing distinct agents for different partitions. Second, a multi-agent deep reinforcement learning guided automatic parameter searching process is designed. The process of adjusting the parameter search direction by perceiving the clustering environment is modeled as a Markov decision process. Using a weakly-supervised reward training policy network, each agent adaptively learns the optimal clustering parameters by interacting with the clusters. Third, a recursive search mechanism adaptable to the data's scale is presented, enabling efficient and controlled exploration of large parameter spaces. Extensive experiments are conducted on nine artificial datasets and a real-world dataset. The results of offline and online tasks show that AR-DBSCAN not only improves clustering accuracy by up to 144.1% and 175.3% in the NMI and ARI metrics, respectively, but also is capable of robustly finding dominant parameters.

An Analysis of AR-DBSCAN: Multi-agent Reinforcement Learning for Enhanced Clustering

The presented paper delineates a sophisticated framework, Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning (AR-DBSCAN), aiming to address several inherent limitations in traditional DBSCAN clustering algorithms, particularly when faced with datasets exhibiting varying density scales. This paper situates itself at the intersection of density-based clustering challenges and advanced machine learning methodologies, offering a significant contribution by employing multi-agent deep reinforcement learning techniques to adaptively and autonomously tune clustering parameters.

Summary of AR-DBSCAN

The core innovation of AR-DBSCAN lies in its multi-agent reinforcement learning framework, which facilitates adaptive clustering across datasets of variable densities. Traditional DBSCAN methods often falter due to the necessity of manually selecting two crucial parameters: Eps and MinPts, especially in heterogeneous density environments. AR-DBSCAN circumvents these challenges by deploying an automatic parameter search process driven by structured entropy-based agent allocation and a recursive search mechanism.

Key Methodological Contributions

1. Agent Allocation via Structural Entropy: The initial dataset is modeled into a structured k-NN graph, and a two-level encoding tree is constructed to quantify information uncertainty through structural entropy. This granulation facilitates the assignment of separate agents to different density partitions within the dataset, thereby decentralizing the parameter optimization task.
2. Multi-agent Reinforcement Learning: Each agent, operating within its assigned partition, utilizes a multi-agent deep reinforcement learning method where parameter searching is modeled as a Markov decision process. Through weakly-supervised rewards and an attention mechanism, the agents iteratively learn to tune Eps and MinPts for their respective partitions.
3. Recursive Mechanism: A recursive structure incrementally focuses and narrows the parameter space, improving search efficiency while maintaining robustness. This effectively reduces the computational burden and enhances clustering precision.

Experimental Validation

The experimental rigor in the paper is demonstrated across nine synthetic datasets and one real-world dataset, validating the effectiveness of AR-DBSCAN. The results indicate significant performance improvements with enhancements in clustering accuracy by up to 144.1% in NMI and 175.3% in ARI when compared to existing baseline methods. The reduction in variance on both metrics further underscores the robustness of AR-DBSCAN in clustering tasks.

Implications and Future Outlook

The implications of AR-DBSCAN are multifaceted. Practically, the framework holds promise for deployment in diverse domains such as image segmentation, biological analysis, and network security, where clustering of spatial data with noise and varying densities is equally critical. Theoretically, this approach provides a robust mechanism for parameter selection free from manual intervention, pushing the boundaries of what is achievable with unsupervised clustering algorithms.

Future developments could explore enhancing the interaction dynamics among agents, expanding AR-DBSCAN's applicability to even more complex and scalable datasets. Additionally, integrating further domain-specific knowledge could facilitate more sophisticated partitioning strategies, enriching the model’s adaptability.

In conclusion, AR-DBSCAN sets a promising direction in autonomously parameter-tuned clustering by seamlessly integrating multi-agent systems with reinforcement learning, significantly advancing the clustering field towards greater autonomy and precision.