seniorK-Means Clustering
How does K-Means behave when features have different units and scales?
Updated May 16, 2026
Short answer
Features with larger numeric scales dominate distance calculations, skewing clustering results.
Deep explanation
K-Means relies on Euclidean distance, so variables like income (0–100000) overpower variables like age (0–100). This leads to centroids being influenced primarily by high-magnitude features unless normalization is applied.
Real-world example
Clustering users where salary dominates behavioral metrics.
Common mistakes
- Skipping normalization step.
Follow-up questions
- What scaling is best?
- Can scaling change cluster structure?