seniorK-Means Clustering
Why does K-Means struggle with categorical data?
Updated May 16, 2026
Short answer
K-Means relies on Euclidean distance, which is not meaningful for categorical variables.
Deep explanation
Categorical data lacks a natural ordering or numeric distance. Using Euclidean distance on encoded categories introduces artificial geometry, leading to incorrect centroid calculations and misleading clusters.
Real-world example
Clustering users by country or device type incorrectly.
Common mistakes
- Applying K-Means directly on label-encoded categories.
Follow-up questions
- What algorithms handle categorical data?
- Why is one-hot encoding not enough?