How to detect duplicate data?

Question

I have got a simple contacts database but I\'m having problems with users entering in duplicate data. I have implemented a simple data comparison but unfortunately the duplicate

Answer 1

For those wandering around the web and end up here, might I suggest that you try using a Google Sheet add-on I created called Flookup. It's particularly good with names and it has a couple of other awesome features which I'll describe below:

1. Say you have a list of names and there are 2 people called "John Smith". You can use the rank parameter from Flookup to instruct the algorithm to return the 1st, 2nd, 3rd or nth best match. This is helpful if you have additional information that you can use to identify the "John Smith" you want.
2. Say you have an additional database/list of apartment numbers. You an specify which "John Smith" you want by typing: John Smith & Apartment A or John Smith & Apartment B as the lookup parameter to help distinguish between the two names.

I hope you find Flookup as beneficial as others have.

Answer 2

This may or may not be related but, minor misspellings might be detected by a Soundex search, e.g., this will allow you to consider Britney Spears, Britanny Spares, and Britny Spears as duplicates.

Nickname contractions, however, are difficult to consider as duplicates and I doubt if it is wise. There are bound to be multiple people named Bill Smith and William Smith, and you would have to iterate that with Charles->Chuck, Robert->Bob, etc.

Also, if you are considering, say, Muslim users, the problems become more difficult (there are too many Muslims, for example, that are named Mohammed/Mohammad).

Answer 3

While I do not have an algorithm for you, my first action would be to take a look at the process involved in entering a new contact. Perhaps users do not have an easy way to find the contact they are looking for. Much like on Stack Overflow's new question form, you could suggest contacts that already exist on the new contact screen.

Answer 4

I'm not sure it will work well for the names vs nicknames problem, but the most common algorithm in this sort of area would be the edit distance / Levenshtein distance algorithm. It's basically a count of the number of character changes, additions and removals required to turn one item into another.

For names, I'm not sure you're ever going to get good results with a purely algorithmic approach - What you really need is masses of data. Take, for example, how much better Google spelling suggestions are than those in a normal desktop application. This is because Google can process billions of web queries and look at what queries lead to each other, what 'did you mean' links actually get clicked etc.

There are a few companies which specialise in the name matching problem (mostly for national security and fraud applications). The one I could remember, Search Software America seems to have been bought out by these guys http://www.informatica.com/products_services/identity_resolution/Pages/index.aspx, but I suspect any of these sorts of solutions would be far to expensive for a contacts application.

Answer 5

You can compare the names with the Levenshtein distance. If the names are the same, the distance is 0, else it is given by the minimum number of operations needed to transform one string into the other.

Answer 6

FullContact.com has API's that can solve this for you, see their documentation here: http://www.fullcontact.com/developer/docs/?category=name.

They have APIs for Name Normalization (Bill into William), Name Deducer (for raw text), and Name Similarity (comparing two names).

All APIs are free at the moment, it could be a good way to get started.