DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that groups together data points that are close to each other, based on a specified distance and density criteria. It identifies core points with many neighbors within a certain radius, forming the centers of clusters. Points near these cores are included in the same cluster, while isolated points with few neighbors are considered noise or outliers. This method is useful for discovering clusters of arbitrary shape and handling noise without predefined cluster counts. It’s widely used in spatial analysis, image processing, and pattern recognition.