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Statistics > Machine Learning

arXiv:2212.12189 (stat)
[Submitted on 23 Dec 2022]

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

Authors:Erich Schubert
View a PDF of the paper titled Stop using the elbow criterion for
k-means and how to choose the number of clusters instead, by Erich
Schubert
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    Abstract:A major challenge when using k-means clustering often is
    how to choose the parameter k, the number of clusters. In this
    letter, we want to point out that it is very easy to draw poor
    conclusions from a common heuristic, the "elbow method". Better
    alternatives have been known in literature for a long time, and
    we want to draw attention to some of these easy to use options,
    that often perform better. This letter is a call to stop using
    the elbow method altogether, because it severely lacks theoretic
    support, and we want to encourage educators to discuss the
    problems of the method -- if introducing it in class at all --
    and teach alternatives instead, while researchers and reviewers
    should reject conclusions drawn from the elbow method.

Subjects:    Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as:     arXiv:2212.12189 [stat.ML]
             (or arXiv:2212.12189v1 [stat.ML] for this version)
             https://doi.org/10.48550/arXiv.2212.12189
             Focus to learn more
             arXiv-issued DOI via DataCite
             https://doi.org/10.1145/3606274.3606278
Related DOI: Focus to learn more
             DOI(s) linking to related resources

Submission history

From: Erich Schubert [view email]
[v1] Fri, 23 Dec 2022 08:15:17 UTC (1,007 KB)
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    Erich Schubert
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