What is the elbow method in clustering?

Updated May 15, 2026

Short answer

The elbow method helps determine the optimal number of clusters in K-Means.

Deep explanation

It plots inertia (within-cluster sum of squares) against number of clusters. The point where improvement slows significantly (elbow point) is chosen as optimal K.

Real-world example

Choosing number of customer segments in retail.

Common mistakes

Choosing K without validation.

Follow-up questions

What if elbow is not clear?

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