seniorK-Means Clustering
How does K-Means behave in high-dimensional spaces?
Updated May 16, 2026
Short answer
K-Means degrades in high dimensions due to distance concentration and reduced separation between points.
Deep explanation
In high-dimensional spaces, Euclidean distances become less meaningful because distances between all points tend to converge. This reduces cluster separability and makes centroid assignment noisy and unstable.
Real-world example
Text embeddings or genomic data clustering.
Common mistakes
- Applying K-Means directly on raw high-dimensional embeddings.
Follow-up questions
- What is the curse of dimensionality?
- How to fix it?