Fuzzy matching deduplication in less than exponential time?
I have a large database (potentially in the millions of records) with relatively short strings of text (on the order of street address, names, etc).
I am looking for a strategy to remove inexact duplicates, and fuzzy matching seems to be the method of choice. My issue: many articles and SO questions deal with matching a single string against all records in a database. I am looking to deduplicate the entire database at once.
The former would be a linear time problem (comparing a value against a million other values, calculating some similarity measure each time). The latter is an exponential time problem (compare every record's values against every other record's value; for a million records, that's approx 5 x 10^11 calculations vs the 1,000,000 calculations for the former option).
I'm wondering if there is another approach than the "brute-force" method I mentioned. I was thinking of possibly generating a string to compare each record's value against, and then group strings that had roughly equal similarity measures, and then run the brute-force method through these groups. I wouldn't achieve linear time, but it might help. Also, if I'm thinking through this properly, this could miss a potential fuzzy match between strings A and B because the their similarity to string C (the generated check-string) is very different despite being very similar to each other.
Any ideas?
P.S. I realize I may have used the wrong terms for time complexity - it is a concept that I have a basic grasp of, but not well enough so I could drop an algorithm into the proper category on the spot. If I used the terms wrong, I welcome corrections, but hopefully I got my point across at least.
Edit
Some commenters have asked, given fuzzy matches between records, what my strategy was to choose which ones to delete (i.e. given "foo", "boo", and "coo", which would be marked the duplicate and deleted). I should note that I am not looking for an automatic delete here. The idea is to flag potential duplicates in a 60+ million record database for human review and assessment purposes. It is okay if there are some false positives, as long as it is a roughly predictable / consistent amount. I just need to get a handle on how pervasive the duplicates are. But if the fuzzy matching pass-through takes a month to run, this isn't even an option in the first place.
Answer
Have a look at http://en.wikipedia.org/wiki/Locality-sensitive_hashing. One very simple approach would be to divide up each address (or whatever) into a set of overlapping n-grams. This STACKOVERFLOW becomes the set {STACKO, TACKO, ACKOV, CKOVE... , RFLOW}. Then use a large hash-table or sort-merge to find colliding n-grams and check collisions with a fuzzy matcher. Thus STACKOVERFLOW and SXACKOVRVLOX will collide because both are associated with the colliding n-gram ACKOV.
A next level up in sophistication is to pick an random hash function - e.g. HMAC with an arbitrary key, and of the n-grams you find, keep only the one with the smallest hashed value. Then you have to keep track of fewer n-grams, but will only see a match if the smallest hashed value in both cases is ACKOV. There is obviously a trade-off here between the length of the n-gram and the probability of false hits. In fact, what people seem to do is to make n quite small and get higher precision by concatenating the results from more than one hash function in the same record, so you need to get a match in multiple different hash functions at the same time - I presume the probabilities work out better this way. Try googling for "duplicate detection minhash"