DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that identifies dense regions of data points, separating them from areas of lower density which are considered noise. It is particularly effective for discovering clusters of arbitrary shape and is robust to outliers, making it useful for data with varying densities and noise levels.