unsupervised

What I do

• Discover hidden patterns in data
• Group similar observations without labels
• Reduce feature dimensions
• Find outliers and anomalies
• Learn latent representations
• Generate synthetic data

When to use me

Use me when:

• Labels are unavailable or expensive
• Exploring unknown data patterns
• Reducing data complexity
• Preprocessing for supervised learning
• Real-time clustering needed

Key Concepts

Clustering

• K-Means: Centroid-based, assumes spherical clusters
• Hierarchical: Dendrogram-based, any distance
• DBSCAN: Density-based, finds arbitrary shapes
• GMM: Probabilistic, soft clustering

Dimensionality Reduction

• PCA: Linear, variance-based
• t-SNE: Non-linear, visualization
• UMAP: Non-linear, structure preservation
• Autoencoders: Non-linear, learned

Evaluation

• Silhouette score
• Davies-Bouldin index
• Calinski-Harabasz index
• Reconstruction error (for autoencoders)