midClustering
What is centroid initialization problem in K-Means?
Updated May 15, 2026
Short answer
Poor centroid initialization can lead K-Means to suboptimal clustering results.
Deep explanation
Random initialization may cause convergence to local minima. K-Means++ improves this by selecting better spaced initial centroids.
Real-world example
Customer clustering producing different results each run.
Common mistakes
- Using random initialization in production models.
Follow-up questions
- What is K-Means++?