Home >Database >Mysql Tutorial >How Can Fuzzy Matching with Levenshtein Distance in MySQL Improve Company Name Auto-Completion?
Background:
Users input company names as part of a large string, and your system needs to automate the matching process with an existing database of company names. While straightforward string matching can be slow, finding the optimal solution for maintaining accuracy and performance is crucial.
Soundex Indexing:
SOUNDEX() is a MySQL function that generates a phonetic representation of a string based on its first few characters. This can help speed up searches for similar-sounding company names. However, it faces certain limitations:
Levenshtein Distance:
The Levenshtein distance is a more advanced measure of string similarity that considers insertions, deletions, and substitutions in strings. This approach provides greater flexibility but requires more computation.
Implementation:
To implement fuzzy matching with Levenshtein distance in MySQL, you can use a stored function like the one available at codejanitor.com. This function takes two strings as input and returns their Levenshtein distance.
Example Matching:
// Calculate the Levenshtein distance between two names $distance = levenshtein_distance('Microsoft', 'Microssift'); // Define a threshold for acceptable matches $max_distance = 2; if ($distance <= $max_distance) { // Suggest the closest match from your database $fuzzy_match = get_fuzzy_match('Microssift'); } else { // No close match found, suggest no matches $fuzzy_match = array(); }
Additional Considerations:
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