# Matchcode Optimization:Jaccard Similarity Coefficient

## Jaccard Similarity

### Specifics

Jaccard Index

### Summary

Jaccard Similarity Index is defined as the size of the intersection divided by the size of the union of the sample sets.

### Returns

Percentage of similarity

Intersection/Union

NGRAM is defined as the length of common strings this algorithm looks for. Matchup default is NGRAM = 2. For “ABCD” vs “GABCE”, Matching NGRAMS would be “AB” and “BC”.

Intersection is defined as the number of common NGRAMS and union is the total number of NGRAMS in the universe of the two strings.

### Example Matchcode Component

### Example Data

STRING1 STRING2 RESULT Johnson Jhnsn Unique Mild Hatter Mild Hatter Wks Match Found Beaumarchais Bumarchay Unique Apco Oil Lube 170 Apco Oil Lube 342 Match Found

Performance | |||||
---|---|---|---|---|---|

Slower | Faster | ||||

Matches | |||||

More Matches | Greater Accuracy |

### Recommended Usage

Hybrid deduper, where a single incoming record can quickly be evaluated independently against each record in an existing large master database. Databases created with abbreviations or similar word substitutions.

### Not Recommended For

Large or Enterprise level batch runs. Since the algorithm must be evaluated for each record comparison, throughput will be very slow.

Databases created via real-time data entry where audio likeness errors are introduced.

### Do Not Use With

UTF-8 data. This algorithm was ported to MatchUp with the assumption that a character equals one byte, and therefore results may not be accurate if the data contains multi-byte characters.