This is a variation on my question Machine learning to catch fraud
I have a data set with about 5 million rows The data set contains names and addresses of companies The name of the company is free text as is are the addresses It seems some of the companies have very bad data. It has been suggested by my manager that a script is generating the information and is varying it slightly each time a company uses our services
The records look something like below Customer Name: ASDFGH KL: Shipper Contact Name ASDFLKJ:
Again its not neccessary that Customer and Shipper Name are roughly the same
I was wondering is there a machine learning algorthim that could flag these customers that anyone is aware of that looks like the users are just inputting random letters like above (To be fair, in this example, the letters are not random at all but all taken from the third row of the keyboard)
I was thinking of adapting spam filtering to try and identify the customers by first tokenizing the Customer Name and generating N-Grams and then classifying it as SPAM or NON SPAM using something like bayes. For this i would need a very large corpus which would neccesitate finding them in the original data set which could be very time consuming. I currently have a starting point of ten records where this is the case
Does anyone know if there is any method or theory to catch people submitting random letters?