Clustering
Phase: 2 | Status: ✅ Complete | Prerequisites: Linear Algebra, Probability & Statistics
Overview
Unsupervised grouping of data points via three complementary approaches: K-Means (centroid-based, minimises within-cluster sum of squares), DBSCAN (density-based, finds arbitrarily shaped clusters and noise), and Gaussian Mixture Models with EM (probabilistic soft clustering).
Contents
| # | File | Type | Description |
|---|---|---|---|
| 1 | theory.md |
Theory | K-Means objective & coordinate descent derivation, DBSCAN density definitions, GMM with EM derivation, failure cases |
| 2 | first_principles.ipynb |
Computation | From-scratch K-Means (random + K-Means++ init), DBSCAN, GMM/EM; convergence visualisation; elbow method; sklearn comparison; failure experiments |
| 3 | exercises.ipynb |
Practice | Hand K-Means iteration, K-Means++ coding task, conceptual analysis of method tradeoffs |
Connections
- Prereqs: Linear Algebra, Probability & Statistics
- Related: 10 PCA (dimensionality reduction before clustering), 07 KNN (distance-based, supervised counterpart)
- Synthesis: Supervised vs. Unsupervised
- Next: 12 Dimensionality Reduction, 13 Neural Networks