DBSCAN Clustering Implementation
This file contains a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering code that helps you perform density-based data clustering. The implementation requires three input parameters: your dataset (feature matrix), the minimum number of points required to form a dense region (minPts), and the neighborhood search radius (epsilon). The algorithm automatically identifies core points, border points, and noise points while handling clusters of arbitrary shapes.