I would like to classify/categorize/cluster/group together millions of rows of merchant names to their standardized merchant name. For example, 1. Walmart 2. Walmart NY 3. Walmart #12 AHN 4. Wal3mart 5. Sam's Club
all belong to the standard name of "WALMART". I have several millions of rows of merchant names, and standard names (close to 60k) and every month new merchant names come in. The merchant names can be spelling errors, subsidiaries of a bigger merchant, merger and acquisition, short cut etc.
Is there a way, we can train a machine learning algorithm to classify these business names.
My preliminary idea is to represent all the merchant names belonging to one standardized name as a group of vectors and then use Support Vector Machines to draw a hyper-plane between all the different standardized merchant names and when a new merchant name comes, represent it as a vector and see which standardized merchant name group, this new merchant name is closest to using a similarity score (say cosine distance).
However, I would like to know if there is any other way of representing these merchant names as features on any other algorithm that i can use for this problem statement. Any brain=storming would be much appreciated. Thanks in advance