levenshtein-distance

I am trying to match a single search term against a dictionary of possible matches using a Levenshtein distance algorithm. The algorithm returns a distance expressed as number of operations required to convert the search string into the matched string. I want to present the results in ranked percentage list of top "N" (say 10) matches. Since the search string can be longer or shorter than the individual dictionary strings, what would be an appropriate logic for expressing the distance as a percentage, which would qualitatively refelct how close "as a percentage" is each result to the query

I need to automatically match product names (cameras, laptops, tv-s etc) that come from different sources to a canonical name in the database. For example "Canon PowerShot a20IS" , "NEW powershot A20 IS from Canon" and "Digital Camera Canon PS A20IS" should all match "Canon PowerShot A20 IS" . I've worked with levenshtein distance with some added heuristics (removing obvious common words, assigning higher cost to number changes etc), which works to some extent, but not well enough unfortunately. The main problem is that even single-letter changes in relevant keywords can make a huge difference

I have a use case where I need to do fuzzy matching of millions of records from multiple files. I identified two algorithms for that: Jaro-Winkler and Levenshtein edit distance. When I started exploring both, I was not able to understand what the exact difference is between the two. It seems Levenshtein gives the number of edits between two strings, and Jaro-Winkler gives a matching score between 0.0 to 1.0. I didn't understand the algorithm. As I need to use either algorithm, I need to know the exact differences with respect to algorithm performance. Levenshtein counts the number of edits

I have two vector of type character in R. I want to be able to compare the reference list to the raw character list using jarowinkler and assign a % similarity score. So for example if i have 10 reference items and twenty raw data items, i want to be able to get the best score for the comparison and what the algorithm matched it to (so 2 vectors of 10). If i have raw data of size 8 and 10 reference items, i should only end up with a 2 vector result of 8 items with the best match and score per item item , match , matched_to ice, 78, ice-cream Below is my code which isn't much to look at.

I'm posting this in the spirit of answering your own questions. The question I had was: How can I implement the Levenshtein algorithm for calculating edit-distance between two strings, as described here , in Delphi? Just a note on performance: This thing is very fast. On my desktop (2.33 Ghz dual-core, 2GB ram, WinXP), I can run through an array of 100K strings in less than one second. function EditDistance(s, t: string): integer; var d : array of array of integer; i,j,cost : integer; begin { Compute the edit-distance between two strings. Algorithm and description may be found at either of