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- A typographical matching algorithm, and a variation of the Accurate Near and Levenshtein algorithms. Levenshtein and Needleman-Wunsch are identical except that character mistakes are given different weights depending on how far two characters are on a standard keyboard layout. For example: A to S is given a mistake weight of 0.4, while A to D is a 0.6 and A to P is a 1.0. Because the algorithm creates a 2D array to determine the distance between two strings, results will be more accurate than Fast Near at expense of throughput.
- Boolean ‘match’ if the normalized distance between two strings is less than the configured scale, where distance is defined as the count of the number of incorrect characters, insertions, and deletions.
Example Matchcode Component
STRING1 STRING2 RESULT Johnson Jhnsn Match Found Maguire Mcguire Match Found Deanardo Dinardio Unique Dearobier Des Robiere Match Found
|More Matches||Greater Accuracy|
- Hybrid or real-time deduping. This works best in matching words that don't match because of a few typographical errors and where the accuracy in duplicates caught outweighs performance concerns.
Not Recommended For
- Enterprise size processes.
- Gather/scatter, survivorship, or record consolidation of sensitive data.
- Quantifiable data or records with proprietary keywords not associated in our knowledgebase tables.
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.