**Example**: Customer segmentation—K-means on (frequency, recency, spend). Segments: high-value loyal, at-risk, new.
**Why Clustering**: Unsupervised; discovers patterns without labels. K-means iteratively assigns to nearest centroid.
**Scale**: MinHash/LSH for large data. Normalize features; choose K via elbow/silhouette....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Ford. The answer also includes follow-up discussion points that interviewers commonly explore.
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