Two-Dimensional DBSCAN Clustering Algorithm with Implementation Details

The two-dimensional DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is a density-based clustering method. It requires three input parameters: an array of (x,y) coordinates, a search radius Eps, and a density threshold Minpts. The algorithm processes the input dataset through core density reachability calculations and outputs clusters as arrays where each row represents a cluster containing the original dataset IDs of its member points. From an implementation perspective, DBSCAN operates by first identifying core points that have at least Minpts neighbors within the Eps radius. The algorithm then expands clusters from these core points through density-connected points while marking outliers as noise. Key functions typically include neighborhood searches using distance matrices or spatial indexing structures like KD-trees for efficiency. The DBSCAN algorithm features straightforward operational steps and effectively handles large-scale datasets. Due to its ability to discover arbitrarily shaped clusters and identify noise points, it is widely applied in data mining and machine learning domains. The implementation typically involves iterative point classification and cluster propagation logic that can be optimized for 2D spatial data processing.