I Use This When...
<!-- Practical use case -->History
Ester, Kriegel, Sander, Xu (1996). Awarded 'Test of Time' award at KDD 2014. Handles arbitrary shapes and noise.
Why It Exists
k-Means assumes round clusters. Real data is messier — irregular shapes, noise points that belong to no cluster. DBSCAN finds dense regions of any shape.
How It Works
Visual Intuition
<!-- 3B1B-style animation description -->Step by Step
<!-- Algorithm walkthrough -->Code
# Implementation sketch
The Math Inside
Core point: has >= minPts within eps radius. Border point: within eps of a core point. Noise: neither. Clusters = connected components of core points.