seniorK-Means Clustering
How does initialization sensitivity affect final K-Means results?
Updated May 16, 2026
Short answer
Different initial centroids can lead to completely different final cluster assignments due to non-convex optimization.
Deep explanation
K-Means minimizes a non-convex objective, meaning multiple valid local minima exist. Initialization determines which basin of attraction the algorithm falls into.
Real-world example
Different segmentation results for the same customer dataset across runs.
Common mistakes
- Assuming deterministic clustering output.
Follow-up questions
- What is K-Means++ solving?
- Why multiple runs help?