ML Unsupervised Clustering

Unsupervised learning finds hidden structures in data without explicit labels.

Core Techniques

• K-Means: Simple and effective for spherical clusters. Use the "Elbow Method" to find the optimal k.
• DBSCAN: Density-based clustering that can handle irregular shapes and noise.
• PCA (Principal Component Analysis): Reducing feature space while retaining variance. Essential for visualization and avoiding the "curse of dimensionality."

Evaluation Metrics

Since there are no labels, we use:

• Silhouette Score: Measuring cluster separation and cohesion.
• Inertia: Sum of squared distances to cluster centers.

Best Practices

• Feature Scaling: Essential for distance-based algorithms like K-Means.
• Interpretablity: Plot your results (e.g., using t-SNE or UMAP for high-dimensional data).