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A major challenge when using k-means clustering often is how to choose theparameter k, the number of clusters. In this letter, we want to point out thatit is very easy to draw poor conclusions from a common heuristic, the "elbowmethod". Better alternatives have been known in literature for a long time, andwe want to draw attention to some of these easy to use options, that oftenperform better. This letter is a call to stop using the elbow methodaltogether, because it severely lacks theoretic support, and we want toencourage educators to discuss the problems of the method -- if introducing itin class at all -- and teach alternatives instead, while researchers andreviewers should reject conclusions drawn from the elbow method.

Stop using the elbow criterion for k-means and how to choose the number of clusters instead