DBSCAN is a density-based clustering algorithm that groups together densely populated areas of points. It requires two parameters: epsilon, which defines the neighborhood distance, and MinPts, the minimum number of points required to form a cluster. DBSCAN iteratively retrieves all points density-reachable from each point and forms clusters from core points with sufficient neighbors within epsilon distance. It can find clusters of arbitrary shape and handle noise without requiring the number of clusters to be specified.