🧩 01 — Clustering Overview¶
Clustering is unsupervised learning that groups similar rows together.
Examples:
- customer segments
- product groups
- anomaly detection
- document/topic grouping
No Target Column¶
The model discovers structure without labels.
Common Algorithms¶
| Algorithm | Idea |
|---|---|
| K-Means | assign points to nearest center |
| Hierarchical | build nested clusters |
| DBSCAN | find dense regions and noise |
Important Warning¶
Clusters are not automatically meaningful. You must profile them and interpret whether they make business sense.
Next¶
➡️ 02-k-means