DBSCAN Clustering Algorithm Simulation Code
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a representative density-based clustering algorithm. Unlike partitioning and hierarchical clustering methods, it defines clusters as the largest sets of density-connected points. DBSCAN can identify clusters of arbitrary shapes in spatial databases with noise by grouping regions with sufficient density. The algorithm implementation typically involves calculating point densities using neighborhood radius (eps) and minimum points (minPts) parameters, followed by expanding clusters through density-reachable connections.