🔗 Jaccard Index Calculator

Measure similarity and diversity between finite sample sets.

Set Similarity Calculator

Enter comma-separated items for each set to calculate Jaccard similarity.

Set A

Items: 4 | Unique: apple, banana, cherry, date

Set B

Items: 4 | Unique: banana, cherry, elderberry, fig

Jaccard Similarity

0.0000

0.0%

Jaccard Distance

0.0000

0.0%

Set Operations:

Intersection (A ∩ B):

No common elements(0 items)

Union (A ∪ B):

(0 items)

Jaccard Index = |A ∩ B| / |A ∪ B| = 0 / 0 = 0.0000

About Jaccard Index

The Jaccard Index measures similarity between finite sample sets, defined as the size of the intersection divided by the size of the union.

Formula:

J(A,B) = |A ∩ B| / |A ∪ B|

Range: 0 to 1
1: Identical sets
0: No common elements
Distance: 1 - Jaccard Index

Use Cases

• Document similarity and plagiarism detection
• Recommendation systems (collaborative filtering)
• Market basket analysis
• Ecology (species similarity between habitats)
• Social network analysis
• Image segmentation evaluation
• A/B testing and user behavior analysis

Examples

Set A: apple, banana, cherry

Set B: banana, cherry, date

Jaccard: 2/4 = 0.5

Documents: Word overlap

Users: Shared preferences

Higher = More Similar

Pro Tip: Jaccard Index is particularly useful when the size of sets matters less than their overlap. It's commonly used in recommendation systems to find users with similar preferences.