Why does K-Means fail on non-spherical clusters?
Updated May 15, 2026
Short answer
K-Means assumes spherical clusters, so it fails on complex shapes.
Deep explanation
It minimizes distance to centroids, which cannot represent curved or density-based clusters.
Real-world example
Failing to cluster crescent-shaped customer groups.
Common mistakes
- Using KMeans for all clustering problems.
Follow-up questions
- What is better alternative?
- Why does shape matter?