DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a method used to group similar data points based on their density in a space. It identifies clusters as areas where points are closely packed together, separated by regions of lower density. Points in dense areas are grouped together, while isolated points in sparse regions are considered noise or outliers. This approach is useful for finding clusters of arbitrary shapes and handling noise, making it valuable in fields like geographical analysis, image processing, and anomaly detection. It requires defining parameters for the neighborhood size and density threshold to identify meaningful groupings.