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 .…