DBSCAN

DBSCAN, or Density-Based Spatial Clustering of Applications with Noise, is a popular clustering algorithm used in data science and machine learning. It groups together points that are closely packed together while marking points in low-density regions as outliers. One of its main characteristics is its ability to identify clusters of varying shapes and sizes, unlike traditional clustering methods such as K-means. DBSCAN is particularly useful in scenarios where the data contains noise and when the number of clusters is not known a priori. Common use cases include anomaly detection, geographic data analysis, and image processing.