I know of the Jaccard index and the Sørensen-Dice coefficient for computing set similarity, but have been unable to find any other algorithms related to set similarity. This site contains quite a few resources for vector similarity, but that's not what I want.

What other set-similarity measures exist?

  • $\begingroup$ You can treat being in a set as an variable (or being in the first set as a classifier and being in the second as the concept). Using this reduction you can apply all the supervised learning metrics - accuracy, mutual information, etc. $\endgroup$
    – DaL
    Commented Jun 15, 2017 at 7:14

1 Answer 1


Other measures are:

  • Overlap Coefficient: $\frac{|A \cap B|}{min(|A|,|B|)}$
  • Tversky index: $|A\cap B| + \alpha|A\setminus B| + \beta|B \setminus A|$ where $\alpha$ and $\beta$ are positive numbers.
  • 1
    $\begingroup$ Tversky is the generalization of Jaccard and DIce $\endgroup$
    – A_Arnold
    Commented Apr 18, 2019 at 20:27

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