K Means Clustering and Unsupervised Learning Questions

Unsupervised learning finding patterns in data without labeled outcomes. K-Means: a centroid-based algorithm that partitions data into k clusters by iteratively assigning points to the nearest cluster center and updating centers until convergence. Covers algorithm mechanics (initialization including random and k-means++, assignment, centroid update, convergence criteria), distance metrics (Euclidean, Manhattan) and their tradeoffs, choosing k (elbow method using inertia, silhouette score), and known advantages (simple, fast, scalable) and disadvantages (sensitive to initialization, assumes roughly spherical/similarly-sized clusters, sensitive to outliers and feature scaling, struggles with categorical or missing data). Also covers other unsupervised clustering approaches conceptually: hierarchical clustering (agglomerative/divisive, dendrograms, linkage criteria) and DBSCAN (density-based clustering for non-spherical clusters and noise/outlier detection). Depth ranges from core mechanics and tradeoffs to production concerns: scaling to large n/d, handling missing or categorical features, choosing between K-Means and alternatives (GMM, DBSCAN, hierarchical) for a given data shape, and building reproducible, auditable clustering pipelines.