Difference between revisions of "Matchcode Optimization:Soundex"

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(Created page with "{{MatchcodeOptimizationNav |AlgorithmsCollapse= }} ==Soundex== ===Specifics=== *https://en.wikipedia.org/wiki/Soundex ===Summary=== An auditory matching algorithm originally...")
 
 
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==Soundex==
==Soundex==
===Specifics===
===Specifics===
*https://en.wikipedia.org/wiki/Soundex
:*https://en.wikipedia.org/wiki/Soundex


===Summary===
===Summary===
An auditory matching algorithm originally developed by the Department of Immigration in 1917 and later adopted by the USPS. Although the Phonetex algorithm is more accurate, the Soundex algorithm is presented for users who need to create a matchcode that emulates one from another application.
:An auditory matching algorithm originally developed by the Department of Immigration in 1917 and later adopted by the USPS. Although the Phonetex algorithm is more accurate, the Soundex algorithm is presented for users who need to create a matchcode that emulates one from another application.


===Returns===
===Returns===
The Soundex algorithm is a string transformation and comparison-based algorithm and is performed on the keybuilding. For example, JOHNSON would be transformed to "J525" and JHNSN would also be transformed to "J525" which would then be considered a SoundEx match after evaluation.
:The Soundex algorithm is a string transformation and comparison-based algorithm and is performed on the keybuilding. For example, JOHNSON would be transformed to "J525" and JHNSN would also be transformed to "J525" which would then be considered a SoundEx match after evaluation.


===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 Found}}
{{EDTRow|Green|Johnson|Jhnsn|Match Found}}
{{EDTRow|White|Stephenz|Stevens|Match Found}}
{{EDTRow|Green|Stephenz|Stevens|Match Found}}
{{EDTRow|Green|Beaumarchais|Bumarchay|Match Found}}
{{EDTRow|Green|Beaumarchais|Bumarchay|Match Found}}
{{EDTRow|Green|Neumon|Pneumon|Unique}}
{{EDTRow|White|Neumon|Pneumon|Unique}}
}}
}}


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===Recommended Usage===
===Recommended Usage===
Large or enterprise level batch runs where using this algorithm will not prevent efficient clustering. Since the algorithm is performed during keybuilding, throughput will be fast.
:Large or enterprise level batch runs where using this algorithm will not prevent efficient clustering. Since the algorithm is performed during keybuilding, throughput will be fast.


Databases created via real-time data entry where audio likeness errors are introduced.
:Databases created via real-time data entry where audio likeness errors are introduced.


Databases of US and English language origin.
:Databases of US and English language origin.


===Not Recommended For===
===Not Recommended For===
For real-time data entry where audio likeness errors can be introduced and accuracy is of the utmost importance, we recommend Phonetex for greater accuracy.
:For real-time data entry where audio likeness errors can be introduced and accuracy is of the utmost importance, we recommend Phonetex for greater accuracy.


Databases of non-US and non-English language origin.
:Databases of non-US and non-English language origin.


Fields whose content data is of type Dictionary or Quantifiable.
:Fields whose content data is of type Dictionary or Quantifiable.


===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 14:31, 27 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


Soundex

Specifics

Summary

An auditory matching algorithm originally developed by the Department of Immigration in 1917 and later adopted by the USPS. Although the Phonetex algorithm is more accurate, the Soundex algorithm is presented for users who need to create a matchcode that emulates one from another application.

Returns

The Soundex algorithm is a string transformation and comparison-based algorithm and is performed on the keybuilding. For example, JOHNSON would be transformed to "J525" and JHNSN would also be transformed to "J525" which would then be considered a SoundEx match after evaluation.

Example Matchcode Component

MCO Algorithm Soundex.png

Example Data

STRING1 STRING2 RESULT
Johnson Jhnsn Match Found
Stephenz Stevens Match Found
Beaumarchais Bumarchay Match Found
Neumon Pneumon Unique



Performance
Slower Faster
Matches
More Matches Greater Accuracy


Recommended Usage

Large or enterprise level batch runs where using this algorithm will not prevent efficient clustering. Since the algorithm is performed during keybuilding, throughput will be fast.
Databases created via real-time data entry where audio likeness errors are introduced.
Databases of US and English language origin.

Not Recommended For

For real-time data entry where audio likeness errors can be introduced and accuracy is of the utmost importance, we recommend Phonetex for greater accuracy.
Databases of non-US and non-English language origin.
Fields whose content data is of type Dictionary or Quantifiable.

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.