DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. It is a popular clustering algorithm used in data analysis to group together points that are close to each other based on their density. Unlike other clustering methods, DBSCAN can identify clusters of varying shapes and sizes, making it effective for real-world data that may not fit into simple geometric patterns. The algorithm works by defining a neighborhood around each point and counting how many points fall within that area. If a point has enough neighbors, it becomes a core point, and clusters are formed by connecting core points and their neighbors. This allows DBSCAN to also identify outliers, or noise, that do not belong to any cluster.