seniorK-Means Clustering
How would you combine K-Means with other models in a production ML pipeline?
Updated May 16, 2026
Short answer
K-Means is often used as a preprocessing step for feature engineering or downstream supervised models.
Deep explanation
Cluster assignments or distances to centroids can become features for classification or regression models. This helps encode latent structure. K-Means can also be used for data compression, anomaly detection, or stratified sampling.
Real-world example
Using cluster IDs as features in fraud detection systems.
Common mistakes
- Treating clustering as final output instead of feature engineering.
Follow-up questions
- Why use clusters as features?
- What models benefit from this?