juniorK-Means Clustering
What distance metric is used in K-Means and why?
Updated May 16, 2026
Short answer
K-Means typically uses Euclidean distance to assign points to clusters.
Deep explanation
Euclidean distance works well because K-Means minimizes variance within clusters. The centroid is the mean, which is optimal under squared Euclidean distance assumptions.
Real-world example
Grouping similar customers based on numeric features.
Common mistakes
- Using K-Means with categorical data directly.
Follow-up questions
- Can K-Means use other distances?
- Why squared distance?