Finding groups of similar strings in a large set of strings

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I have a reasonably large set of strings (say 100) which has a number of subgroups characterised by their similarity. I am trying to find/design an algorithm which would find theses groups reasonably efficiently.

As an example let's say the input list is on the left below, and the output groups are on the right.

Input                           Output
-----------------               -----------------
Jane Doe                        Mr Philip Roberts
Mr Philip Roberts               Phil Roberts     
Foo McBar                       Philip Roberts   
David Jones                     
Phil Roberts                    Foo McBar        
Davey Jones            =>         
John Smith                      David Jones      
Philip Roberts                  Dave Jones       
Dave Jones                      Davey Jones      
Jonny Smith                     
                                Jane Doe         

                                John Smith       
                                Jonny Smith

Does anybody know of any ways to solve this reasonably efficiently?

The standard method for finding similar strings seems to be the Levenshtein distance, but I can't see how I can make good use of that here without having to compare every string to every other string in the list, and then somehow decide on a difference threshold for deciding if the two strings are in the same group or not.

An alternative would be an algorithm that hashes strings down to an integer, where similar strings hash to integers which are close together on the number-line. I have no idea what algorithm that would be though, if one even exists

Does anybody have any thoughts/pointers?

UPDATE: @Will A: Perhaps names weren't as good an example as I first thought. As a starting point I think I can assume that in the data I will be working with, a small change in a string will not make it jump from one group to another.

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7 Answers

There is a solution for this exact problem documented in an open source java library for fuzzy matching https://github.com/intuit/fuzzy-matcher

The idea used there is to break down the names in words (tokens) and use text matching algorithm to find similarity in words (like Soundex, Jaccard or Lavenshtiein).

Then use the score found from each word and average the score for each name.

Performance for such matching is fairly critical, since if we keep matching every name with every other this becomes an exponentially growing complexity.

This library relies on equivalence and transitive property of the match algorithm Where if "David" matches with "Davey" then the reverse match is implied, and you don't have to run those matches

This library has a few more tricks to reduce the match complexity, and I was able to run a match against 4000 names in around 2 secs.

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