You can start with using SOUNDEX(), this will probably do for what you need (I picture an auto-suggestion box of already-existing alternatives for what the user is typing). The drawbacks of SOUNDEX() are: its inability to differentiate longer strings. Only the first few characters are taken into account, longer strings that diverge at the end … Read more
There are two great vim plugins for this. ctrlp: Written in pure VimL Works pretty much everywhere Supports custom finders for improved performance Most popular fuzzy search plugin for Vim Command-T: Written in C, VimL and Ruby Fast out of the box Requires +ruby support in Vim Recommends Vim version >= 7.3 EDIT: I use … Read more
The new regex library that’s soon supposed to replace re includes fuzzy matching. https://pypi.python.org/pypi/regex/ The fuzzy matching syntax looks fairly expressive, but this would give you a match with one or fewer insertions/additions/deletions. import regex regex.match(‘(amazing){e<=1}’, ‘amaging’)
Considering that you’re trying to do a fuzzy search on a list of school names, I don’t think you want to go for traditional string similarity like Levenshtein distance. My assumption is that you’re taking a user’s input (either keyboard input or spoken over the phone), and you want to quickly find the matching school. … Read more
Commons Lang has an implementation of Levenshtein distance. Commons Codec has an implementation of soundex and metaphone.
I’ve found that the stuff SQL Server gives you to do fuzzy matching is pretty clunky. I’ve had really good luck with my own CLR functions using the Levenshtein distance algorithm and some weighting. Using that algorithm, I’ve then made a UDF called GetSimilarityScore that takes two strings and returns a score between 0.0 and … Read more
I tried using existing fuzzy libraries like fuse.js and also found them to be terrible, so I wrote one which behaves basically like sublime’s search. https://github.com/farzher/fuzzysort The only typo it allows is a transpose. It’s pretty solid (1k stars, 0 issues), very fast, and handles your case easily: fuzzysort.go(‘int’, [‘international’, ‘splint’, ‘tinder’]) // [{highlighted: ‘*int*ernational’, … Read more
Simon White of Catalysoft wrote an article about a very clever algorithm that compares adjacent character pairs that works really well for my purposes: http://www.catalysoft.com/articles/StrikeAMatch.html Simon has a Java version of the algorithm and below I wrote a PL/Ruby version of it (taken from the plain ruby version done in the related forum entry comment … Read more