Matchcode Optimization:Accunear: Difference between revisions
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==Accurate Near== | ==Accurate Near== | ||
===Specifics=== | ===Specifics=== | ||
Accurate Near is a Melissa Data Algorithm largely based on the Levenshtein Distance Algorithm. | :Accurate Near is a Melissa Data Algorithm largely based on the Levenshtein Distance Algorithm. | ||
*https://en.wikipedia.org/wiki/Levenshtein_distance | :*https://en.wikipedia.org/wiki/Levenshtein_distance | ||
===Summary=== | ===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. | :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=== | ===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. | :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 | ===Example Matchcode Component=== | ||
:[[File:MCO_Algorithm_Accunear.png|link=]] | :[[File:MCO_Algorithm_Accunear.png|link=]] | ||
===Example Data | ===Example Data=== | ||
{{ExampleDataTableV1|STRING1|STRING2|RESULT | {{ExampleDataTableV1|STRING1|STRING2|RESULT | ||
|AdditionalRows= | |AdditionalRows= | ||
{{EDTRow| | {{EDTRow|Green|Johnson|Jhnsn|Match Found}} | ||
{{EDTRow| | {{EDTRow|Green|Maguire|Mcguire|Match Found}} | ||
{{EDTRow| | {{EDTRow|White|Deanardo|Dinardio|Unique}} | ||
{{EDTRow| | {{EDTRow|White|34-678 Core|34-678 Reactor|Unique}} | ||
}} | }} | ||
| Line 35: | Line 35: | ||
===Recommended Usage=== | ===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. | :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=== | ===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. | :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=== | ===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. | :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. | ||
[[Category:MatchUp Hub]] | [[Category:MatchUp Hub]] | ||
[[Category:Matchcode Optimization]] | [[Category:Matchcode Optimization]] | ||
Latest revision as of 22:26, 26 September 2018
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 Component
Example Data
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.