DBSCAN

DBSCAN is a density-based clustering algorithm that is especially strong at discovering clusters with irregular shapes. Unlike methods such as K-Means, it does not require the number of clusters to be specified in advance and can explicitly label noise points. This makes it highly valuable in complex data spaces. However, it is sensitive to density-related hyperparameters and may struggle when clusters have very different densities.