Difference between revisions of "Matchcode Optimization:Frequency Near"

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(Created page with "{{MatchcodeOptimizationNav |AlgorithmsCollapse= }} ==Frequency Near== ===Specifics=== The Frequency near algorithm will match the characters of one string to the characters o...")
 
 
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==Frequency Near==
 
==Frequency Near==
 
===Specifics===
 
===Specifics===
The Frequency near algorithm will match the characters of one string to the characters of another without any regard to the sequence while allowing a set number of differences.  
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:The Frequency near algorithm will match the characters of one string to the characters of another without any regard to the sequence while allowing a set number of differences.  
  
 
===Summary===
 
===Summary===
Frequency Near can be used when 2 strings are expected to have the same characters, but might be transposed or have an insertion or deletion. For example "abcdef" would be considered a 100% match to "badcfe" or “badcfx”.
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:Frequency Near can be used when 2 strings are expected to have the same characters, but might be transposed or have an insertion or deletion. For example "abcdef" would be considered a 100% match to "badcfe" or “badcfx”.
  
 
===Returns===
 
===Returns===
Boolean ‘match’ if the compared data has the same values.
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:Boolean ‘match’ if the compared data has the same values.
  
 
===Example Matchcode Component===
 
===Example Matchcode Component===
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{{ExampleDataTableV1|STRING1|STRING2|RESULT
 
{{ExampleDataTableV1|STRING1|STRING2|RESULT
 
|AdditionalRows=
 
|AdditionalRows=
{{EDTRow|White|Johnson|Jhnsn|Match}}
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{{EDTRow|Green|Johnson|Jhnsn|Match}}
 
{{EDTRow|Green|Lynda|Dylan|Match}}
 
{{EDTRow|Green|Lynda|Dylan|Match}}
 
{{EDTRow|Green|A B D H T|A T H D X|Match}}
 
{{EDTRow|Green|A B D H T|A T H D X|Match}}
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===Recommended Usage===
 
===Recommended Usage===
Batch processing—this is a fast algorithm which will identify a greater percentage of duplicates because it will count exact matches and minor character transpositions.
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:Batch processing—this is a fast algorithm which will identify a greater percentage of duplicates because it will count exact matches and minor character transpositions.
  
This algorithm is also recommended when the data is comprised of single character dictionary values like ‘A B C’.
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:This algorithm is also recommended when the data is comprised of single character dictionary values like ‘A B C’.
  
 
===Not Recommended For===
 
===Not Recommended For===
Short name data types where a simple character transformation would represent a different value. This algorithm is also not recommended when trying to identify differences in long strings.
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:Short name data types where a simple character transformation would represent a different value. This algorithm is also not recommended when trying to identify differences in long strings.
  
 
===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.
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: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 23:20, 26 September 2018

← MatchUp Hub

Matchcode Optimization Navigation
Matchcode Optimization
First Component
Fuzzy Algorithms
Swap Matching
Blank Matching
Advanced Component Types
Algorithms
Accunear
Alphas
Consonants
Containment
Dice's Coefficient
Double Metaphone
Exact
Fast Near
Frequency
Frequency Near
Jaccard Similarity Coefficient
Jaro
Jaro-Winkler
Longest Common Substring (LCS)
MD Keyboard
Needleman-Wunsch
N-Gram
Numeric
Overlap Coefficient
Phonetex
Smith-Waterman-Gotoh
Soundex
UTF8 Near
Vowels


Frequency Near

Specifics

The Frequency near algorithm will match the characters of one string to the characters of another without any regard to the sequence while allowing a set number of differences.

Summary

Frequency Near can be used when 2 strings are expected to have the same characters, but might be transposed or have an insertion or deletion. For example "abcdef" would be considered a 100% match to "badcfe" or “badcfx”.

Returns

Boolean ‘match’ if the compared data has the same values.

Example Matchcode Component

MCO Algorithm FrequencyNear.png

Example Data

STRING1 STRING2 RESULT
Johnson Jhnsn Match
Lynda Dylan Match
A B D H T A T H D X Match
A B D H T A T H D B Match



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 because it will count exact matches and minor character transpositions.
This algorithm is also recommended when the data is comprised of single character dictionary values like ‘A B C’.

Not Recommended For

Short name data types where a simple character transformation would represent a different value. This algorithm is also not recommended when trying to identify differences in long strings.

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.