DBSCAN Algorithm Source Code for Clustering Applications

This repository contains a MATLAB implementation of the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm source code, specifically designed for clustering applications. The program implements key components of density-based clustering including Euclidean distance computation between data points, identification of core points based on density thresholds, and recursive expansion of clusters through density-reachable points. The implementation follows the standard DBSCAN workflow: first calculating pairwise distances using vectorized operations, then classifying points as core, border, or noise based on minpts and epsilon parameters. The algorithm efficiently discovers arbitrary-shaped clusters through region query functions that find neighboring points within the specified radius. This source code provides a practical foundation for understanding cluster formation mechanisms and can be integrated into research projects or data analysis pipelines for pattern recognition and outlier detection applications.