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Jaccard Similarity Calculator

Work out jaccard similarity instantly with clear inputs, formula shown and shareable results.

About Jaccard Similarity Calculator

The Jaccard index divides the size of the intersection by the size of the union, so it penalises elements present in only one set. It is the standard measure for shingled documents, MinHash deduplication and tag-set comparison. The overlap coefficient normalises by the smaller set instead, which is more forgiving when one set is much larger than the other.

Formula

Jaccard index

J = |A and B| / |A or B| = intersection / (|A| + |B| - intersection); distance = 1 - J

Worked example

With the default values below, Jaccard Similarity Calculator returns Jaccard similarity of 0.2857. Change any input above and every figure here is recalculated in your browser.

Inputs used

Size of set Aminimum 0
120
Size of set Bminimum 0
150
Elements in both setsminimum 0
60

Results

Jaccard similarity
0.2857
Union size
210
Jaccard distance
0.7143
Overlap coefficient (Szymkiewicz-Simpson)
0.5000

Step by step

  1. 1Values used
  2. 2Jaccard index
  3. 3Jaccard similarity= 0.2857
  4. 4Union size= 210
  5. 5Jaccard distance= 0.7143
  6. 6Overlap coefficient (Szymkiewicz-Simpson)= 0.5000

Assumptions and limits

  • The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 4 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Why is Jaccard low when one set contains the other?

Because the union is the size of the larger set. A 10-element subset of a 100-element set scores 0.1 on Jaccard but 1.0 on the overlap coefficient.

How does MinHash relate to this?

MinHash estimates the Jaccard index from a small number of hash signatures, so near-duplicate detection can run over billions of documents without computing exact intersections.

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