Matchcode Optimization:Fast Near

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Fast Near

Specifics

Fast Near is a Melissa Data algorithm loosely based on the Levenshtein Distance Algorithm, which returns the distance between two strings, where distance is defined as the count of the number of incorrect characters, insertions, and deletions.

Summary

A typographical matching algorithm, Fast Near works best in matching words that don't match because of a few typographical errors. The user specifies (on a scale from 1 to 4, with 1 being the tightest) the degree of similarity between data being matched. The scale is then used as a weight which is adjusted on the length of the strings being. The Fast Near algorithm is a speedy approximation of the Accurate Near algorithm.

Returns

Boolean ‘match or no match’ based on whether the compared data has less than an adjusted number of differences (or more).

Example Matchcode Component

Example Data

STRING1	STRING2	RESULT
Johnson	Jhnsn	Unique
Maguire	Mcguire	Match Found
Deanardo	Dinardio	Match Found
34-678 Core	34-678 Reactor	Unique

Performance
	Slower		Faster
Matches
	More Matches		Greater Accuracy

Recommended Usage

Batch processing: this is a fast algorithm which will identify a greater percentage of duplicates found than other algorithms, but since it is more basic in its routine, sometimes Fast Near will find false matches or miss true matches.

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.