What is K-Means clustering and how does it work?

Updated May 15, 2026

Short answer

K-Means partitions data into K clusters by minimizing distance to cluster centroids.

Deep explanation

K-Means initializes centroids randomly, assigns points to nearest centroid, recalculates centroids, and repeats until convergence. It minimizes within-cluster variance using iterative optimization.

Real-world example

Grouping users based on purchase behavior.

Common mistakes

Not scaling data before applying K-Means.

Follow-up questions

Why does K-Means require normalization?

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