DBSCAN blueprint infographic
PHASE 3 · CLUSTERING ALGORITHM 13 / 30 DENSITY-BASED

DBSCAN

Density-Based Spatial Clustering of Applications with Noise
Core Concept
DBSCAN groups together points that are densely packed and labels lonely points in sparse regions as noise. Unlike k-Means, you never tell it how many clusters to find — it discovers arbitrarily shaped clusters (crescents, rings, blobs) directly from the geometry of the data, and it is naturally robust to outliers.
Key Components

ε (eps)

The neighborhood radius. How close two points must be to count as neighbors.

minPts

Minimum neighbors within ε for a point to qualify as dense (a core point).

Point Types

Core (dense), Border (near a core, sparse), Noise (isolated outlier).

Density-Reachable

Chaining core points by ε-neighborhoods grows a single cluster outward.

How It Works
  1. Pick an unvisited point and count neighbors inside its ε-radius.
  2. If neighbors ≥ minPts, it's a core point → start a new cluster.
  3. Absorb all density-reachable points, expanding the cluster outward.
  4. Points near a core but below minPts become border points.
  5. Points reachable from nothing dense are flagged as noise.
core(p)  ⇔  |Nε(p)| ≥ minPts
Real-World Applications
🗺️ Geospatial hotspot detection 🚨 Anomaly / fraud detection 🛰️ Satellite & LIDAR segmentation 🖼️ Image region grouping 👥 Customer density segments
Checkpoint Questions
  1. Why can DBSCAN find crescent-shaped clusters that k-Means fundamentally cannot?
  2. What happens to your clustering if you set ε too large? Too small?
  3. Distinguish a border point from a noise point — what's the deciding condition?