What is DBSCAN clustering?

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clusters data based on density. It defines clusters as dense regions of points separated by low-density regions. Two hyperparameters: epsilon (ε) — the radius of a neighborhood, and min_samples — minimum number of points to form a dense region. Points are classified as core points, border points, or outliers (noise). Advantages over K-Means: discovers arbitrarily shaped clusters, automatically determines the number of clusters, and identifies outliers. It fails with clusters of varying density.