DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an unsupervised, density-based clustering algorithm that works on the premise that clusters are dense spaces separated by lower-density regions. Unlike K-Means, it does not require the number of clusters to be defined beforehand.

DBSCAN is highly useful for anomaly detection because it is designed to separate out and not assign noise points (outliers) to any cluster. Its common use cases involve separating high-density versus low-density clusters and analyzing non-linear datasets.