seniorK-Means Clustering
What are the core assumptions you must validate before using K-Means?
Updated May 16, 2026
Short answer
You must validate spherical clusters, equal variance, Euclidean relevance, and low outlier presence.
Deep explanation
K-Means assumes clusters are convex, isotropic, and separable in Euclidean space. Violating these assumptions leads to unstable centroids and misleading partitions. Feature scaling and distribution checks are essential before applying it.
Real-world example
Validating whether user behavior data is suitable for segmentation.
Common mistakes
- Applying K-Means without verifying assumptions.
Follow-up questions
- What assumption is most often violated?
- Why does scaling matter?