67. DBSCAN: Clustering That Handles Messy Data

Last post K-Means failed on crescent-shaped data. It cut across the natural curves instead of following them. You also had to tell it K upfront. And one outlier could drag a centroid completely off course. DBSCAN fixes all three problems. It finds clusters based on density, not distance to a centroid. It discovers K automatically. It labels outliers explicitly instead of forcing them into a cluster. Different idea. Different use cases. Worth knowing. What You'll Learn Here How density-based clus