Why is feature scaling critical for K-Means?
Updated May 16, 2026
Short answer
K-Means depends on distance, so unscaled features dominate clustering results.
Deep explanation
Since K-Means uses Euclidean distance, features with larger numeric ranges dominate distance computation. Scaling ensures each feature contributes equally to cluster formation.
Real-world example
Clustering customers using income (large scale) and age (small scale).
Common mistakes
- Running K-Means on raw unnormalized data.
Follow-up questions
- Which scaling method is best?
- Does normalization always help?