k-Means Clustering

Key concepts:

• Initialization (k-means++), inertia, convergence criteria.
• Choosing k: elbow, silhouette; scaling and feature choice.
• Handling outliers and non-spherical clusters.

Common interview checks:

• k-Means vs. GMM; distance metrics.
• Preprocessing and standardization considerations.