juniorK-Means Clustering
What is centroid initialization in K-Means?
Updated May 16, 2026
Short answer
Centroid initialization is the process of selecting initial cluster centers before iterations begin.
Deep explanation
Poor initialization can lead to slow convergence or bad local minima. K-Means++ improves initialization by spreading centroids apart probabilistically, reducing clustering errors.
Real-world example
Better initialization improves segmentation in customer analytics.
Common mistakes
- Using completely random initialization without K-Means++.
Follow-up questions
- Why does initialization matter?
- What is K-Means++?