seniorK-Means Clustering
How does K-Means behave when clusters have unequal densities?
Updated May 16, 2026
Short answer
K-Means performs poorly because it assumes all clusters have similar density and variance.
Deep explanation
K-Means partitions space based on nearest centroids, implicitly assuming equal variance spherical distributions. When one cluster is dense and another is sparse, centroids shift toward dense regions, causing sparse clusters to be underrepresented or split incorrectly.
Real-world example
Customer groups where premium users are tightly packed and casual users are widely spread.
Common mistakes
- Assuming K-Means works well regardless of density distribution.
Follow-up questions
- Which algorithm handles density variation better?
- Why does density matter?