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 clustering works What eps and min_samples actually control Core points, border points, and noise points explained How to tune DBSCAN parameters properly When DBSCAN wins and when K-Means is still better Anomaly detection with DBSCAN Full working code The Core Idea: Density K-Means asks: what's the nearest centroid? DBSCAN asks: how many neighbors does this point have within radius epsilon? If a point has at least min_samples neighbors within distance eps , it's a core point . Core points form the dense heart of a cluster.…