Matchcode Optimization:Double Metaphone: Difference between revisions
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==Double Metaphone== | ==Double Metaphone== | ||
===Specifics=== | ===Specifics=== | ||
*http://en.wikipedia.org/wiki/Double_Metaphone#Double_Metaphone. | |||
:Double Metaphone improves upon the Soundex algorithm by identifying inconsistencies in English spelling and pronunciation to produce a more accurate encoding. | :Double Metaphone improves upon the Soundex algorithm by identifying inconsistencies in English spelling and pronunciation to produce a more accurate encoding. | ||
Revision as of 23:09, 26 September 2018
Double Metaphone
Specifics
- Double Metaphone improves upon the Soundex algorithm by identifying inconsistencies in English spelling and pronunciation to produce a more accurate encoding.
Summary
- A variation of both the SoundEx and PhonetEx algorithms. Double Metaphone performs 2 different PhonetEx-style transformations. It creates two PhonetEx-like strings (primary and alternate) for both strings based on multiple phonetic variations which originated from other languages. Unlike Soundex and Phonetex, the Metaphones are generated during the comparison algorithm.
Returns
- Primary keys match = 99.9%
- Alternate keys match = 85.0%
- Primary matches alternate = 85.0%
- Alternate keys match = 75.0%
Example Matchcode Component
Example Data
STRING1 STRING2 RESULT Johnson Jhnsn Match Found Deanardo Dinardio Match Found Beaumarchais Bumarchay Match Found Theverymost Hteberynost Unique
| 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.
- Databases created via real-time data entry where audio likeness errors are introduced.
- Databases of US and English language origin.
Not Recommended For
- Large or Enterprise level batch runs. Since the algorithm must be evaluated for each record comparison, throughput will be very slow.
- Fields whose content data is of type Dictionary or Quantifiable.
- Databases of non-US and non-English language origin.
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.