Matchcode Optimization:N-Gram
N-Gram
Specifics
Summary
Counts the number of common contiguous sub-strings (grams) between the two strings.
Returns
Percentage of similarity
MatchingGrams/(LongestLength – (NGRAMS - 1))
NGRAM is defined as the length of common strings this algorithm looks for. Matchup default I NGRAM = 2. For “ABCD” vs “GABCE”, Matching NGRAMS would be “AB” and “BC”.
MatchingGrams is the count of the number of matching grams.
LongestLength is the longer string length of the two strings being compared.
Example Matchcode Component
Example Data
STRING1 STRING2 RESULT Johnson Jhnsn Unique Neumon Pneumon 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.
Batch processes where NGRAM is set on a single non-first matchcode component.
Databases created with abbreviations or similar word substitutions.
Multi word field data where a trailing word does not appear in every record in the expected group or data contains acceptable variations of one of the keywords.
Not Recommended For
Databases where the number of errors with relation to the string length result is a small number of common substrings.
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.