Dynamic DBSCAN with Euler Tour Sequences
This addresses the computational bottleneck for real-time clustering in large-scale dynamic applications, offering an incremental improvement over static DBSCAN methods.
The paper tackles the problem of efficiently clustering dynamic datasets with DBSCAN by proposing a fast algorithm that uses Euler Tour Trees to support online updates without reprocessing the entire dataset, achieving a time complexity of O(d log^3(n) + log^4(n)) per update and significant speedups in empirical tests.
We propose a fast and dynamic algorithm for Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that efficiently supports online updates. Traditional DBSCAN algorithms, designed for batch processing, become computationally expensive when applied to dynamic datasets, particularly in large-scale applications where data continuously evolves. To address this challenge, our algorithm leverages the Euler Tour Trees data structure, enabling dynamic clustering updates without the need to reprocess the entire dataset. This approach preserves a near-optimal accuracy in density estimation, as achieved by the state-of-the-art static DBSCAN method (Esfandiari et al., 2021) Our method achieves an improved time complexity of $O(d \log^3(n) + \log^4(n))$ for every data point insertion and deletion, where $n$ and $d$ denote the total number of updates and the data dimension, respectively. Empirical studies also demonstrate significant speedups over conventional DBSCANs in real-time clustering of dynamic datasets, while maintaining comparable or superior clustering quality.