What happens when clusters overlap in K-Means?
Updated May 16, 2026
Short answer
K-Means struggles with overlapping clusters because it forces hard assignments to the nearest centroid.
Deep explanation
K-Means does not model probability or uncertainty. Each point belongs to exactly one cluster, so overlapping distributions cause unstable boundaries. This leads to high misclassification and centroid drift.
Real-world example
Customer segments with similar purchasing behavior blending together.
Common mistakes
- Using K-Means where probabilistic clustering (GMM) is needed.
Follow-up questions
- What algorithm handles overlap better?
- Why is hard assignment limiting?