string-matching

I'm looking for an algorithm that takes 2 strings and will give me back a "factor of similarity". Basically, I will have an input that may be misspelled, have letters transposed, etc, and I have to find the closest match(es) in a list of possible values that I have. This is not for searching in a database. I'll have an in-memory list of 500 or so strings to match against, all under 30 chars, so it can be relatively slow. I know this exists, i've seen it before, but I can't remember its name. Edit: Thanks for pointing out Levenshtein and Hamming. Now, which one should I implement? They

I'm using the 'agrep' function in R, which returns a vector of matches. I would like a function similar to agrep that only returns the best match, or best matches if there are ties. Currently, I am doing this using the 'sdist()' function from the package 'cba' on each element of the resulting vector, but this seems very redundant. /edit: here is the function I'm currently using. I'd like to speed it up, as it seems redundant to calculate distance twice. library(cba) word <- 'test' words <- c('Teest','teeeest','New York City','yeast','text','Test') ClosestMatch <- function(string,StringVector)

I want Regular Expression to accept only Arabic characters , Spaces and Numbers . Numbers are not required to be in Arabic. I found the following expression: ^[\u0621-\u064A]+$ which accepts only only Arabic characters while I need Arabic characters, Spaces and Numbers. Just add 1-9 (in Unicode format) to your character-class: ^[\u0621-\u064A0-9 ]+$ OR add \u0660-\u0669 to the character-class which is the range of Arabic numbers : ^[\u0621-\u064A\u0660-\u0669 ]+$ You can use: ^[\u0621-\u064A\s\p{N}]+$ \p{N} will match any unicode numeric digit. To match only ASCII digit use: ^[\u0621-\u064A\s0

I'm looking for a fuzzy search JavaScript library to filter an array. I've tried using fuzzyset.js and fuse.js , but the results are terrible (there are demos you can try on the linked pages). After doing some reading on Levenshtein distance, it strikes me as a poor approximation of what users are looking for when they type. For those who don't know, the system calculates how many insertions , deletions , and substitutions are needed to make two strings match. One obvious flaw, which is fixed in the Levenshtein-Demerau model, is that both blub and boob are considered equally similar to bulb

I am doing clinical message normalization (spell check) in which I check each given word against 900,000 word medical dictionary. I am more concern about the time complexity/performance. I want to do fuzzy string comparison, but I'm not sure which library to use. Option 1: import Levenshtein Levenshtein.ratio('hello world', 'hello') Result: 0.625 Option 2: import difflib difflib.SequenceMatcher(None, 'hello world', 'hello').ratio() Result: 0.625 In this example both give the same answer. Do you think both perform alike in this case? In case you're interested in a quick visual comparison of

I would like to write a regex for searching for the existence of some words, but their order of appearance doesn't matter. For example, search for "Tim" and "stupid". My regex is Tim.*stupid|stupid.*Tim . But is it possible to write a simpler regex (e.g. so that the two words appear just once in the regex itself)? Unihedron See this regex: /^(?=.*Tim)(?=.*stupid).+/ Regex explanation: ^ Asserts position at start of string. (?=.*Tim) Asserts that "Tim" is present in the string. (?=.*stupid) Asserts that "stupid" is present in the string. .+ Now that our phrases are present, this string is valid