Clustering: k-means, hierarchical
Machine Learning · Engineering
Study notes
Segment customers: k-means with k=3 iterates: assign each customer to nearest centroid, recompute centroids, repeat until stable. Result: budget, regular, premium groups. Hierarchical clustering builds a dendrogram; cutting at a height gives clusters. Elbow method plots inertia vs k, picking the bend.