Reviewed by Aditya Kumar Ā· Last reviewed 2026-03-24
**Example**: Customer segmentationāK-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....
Pro-Move: 'We use clustering for fraudāanomalies fall in small clusters. Validated with known fraud cases.'
Example: Customer segmentationāK-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. Use cases: anomaly detection (outliers far from clusters), recommendations (similar users).
Validation: Domain expert review; interpretable features.
Pro-Move: 'We use clustering for fraudāanomalies fall in small clusters. Validated with known fraud cases.'
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