Matchcode Optimization:Jaro
Jaro
Specifics
Winkler Distance
Summary
Gathers common characters (in order) between the two strings, then counts transpositions between the two common strings.
Returns
Percentage of similarity
1/3 * (common/len1 + common/len2 + (common-transpositions)/common)
Where common is defined as a character match if the distance within the 2 strings is within the algorithms defined range. Transpositions are defined as: a character match (but different sequence order) /2
Example Matchcode Component
Example Data
STRING1 STRING2 RESULT Johnson Jhnsn Match Found Maguire Mcguire Match Found Beaumarchais Bumarchay Unique Deanardo Dinardio Unique
| 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.