Matchcode Optimization:Accunear
Accurate Near
Specifics
Accurate Near is a Melissa Data Algorithm largely based on the Levenshtein Distance Algorithm.
Summary
A typographical matching algorithm. You specify (on a scale from 1 to 4, with 1 being the tightest) the degree of similarity between data being matched. This scale is then used as a weight which is adjusted on the length of the strings being. 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.
Returns
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 Usage 1
Example Data 1
STRING1 STRING2 RESULT Johnson Jhnsn Match Found Maguire Mcguire Match Found Deanardo Dinardio Unique 34-678 Core 34-678 Reactor Unique
| Performance | |||||
|---|---|---|---|---|---|
| Slower | Faster | ||||
| Matches | |||||
| More Matches | Greater Accuracy | ||||
Recommended Usage
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
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.