Difference between revisions of "Matchcode Optimization:Phonetex"

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==Phonetex==
==Phonetex==
===Specifics===
===Specifics===
(pronounced "Fo-NEH-tex") An auditory matching algorithm developed by Melissa Data. It works best in matching words that sound alike but are spelled differently. It is an improvement over the Soundex algorithm.
:(pronounced "Fo-NEH-tex") An auditory matching algorithm developed by Melissa Data. It works best in matching words that sound alike but are spelled differently. It is an improvement over the Soundex algorithm.


===Summary===
===Summary===
A variation of the SoundEx Algorithm. PhonetEx takes into account letter combinations that sound alike, particularly at the start of the word (such as 'PN' = 'N', 'PH' = 'F').
:A variation of the SoundEx Algorithm. PhonetEx takes into account letter combinations that sound alike, particularly at the start of the word (such as 'PN' = 'N', 'PH' = 'F').


===Returns===
===Returns===
The Phonetex algorithm is a string transformation and comparison-based algorithm and is performed on the keybuilding. For example, JOHNSON would be transformed to "J565000000" and JHNSN would also be transformed to "J565000000" which would then be considered a Phonetex match after evaluation.
:The Phonetex algorithm is a string transformation and comparison-based algorithm and is performed on the keybuilding. For example, JOHNSON would be transformed to "J565000000" and JHNSN would also be transformed to "J565000000" which would then be considered a Phonetex match after evaluation.


===Example Matchcode Component===
===Example Matchcode Component===
:[[File:MCO_Algorithm_Phontex.png|link=]]
:[[File:MCO_Algorithm_Phonetex.png|link=]]


===Example Data===
===Example Data===
{{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|Match Found}}
{{EDTRow|Green|Neumon|Pneumon|Match Found}}
Line 34: Line 34:


===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===
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 14:30, 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


Phonetex

Specifics

(pronounced "Fo-NEH-tex") An auditory matching algorithm developed by Melissa Data. It works best in matching words that sound alike but are spelled differently. It is an improvement over the Soundex algorithm.

Summary

A variation of the SoundEx Algorithm. PhonetEx takes into account letter combinations that sound alike, particularly at the start of the word (such as 'PN' = 'N', 'PH' = 'F').

Returns

The Phonetex algorithm is a string transformation and comparison-based algorithm and is performed on the keybuilding. For example, JOHNSON would be transformed to "J565000000" and JHNSN would also be transformed to "J565000000" which would then be considered a Phonetex match after evaluation.

Example Matchcode Component

MCO Algorithm Phonetex.png

Example Data

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



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

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.