I'm relatively new to data science, but an old hat at analytics. I'm looking for some direction on a project that I'm wanting to work on.
I'm working with discrete objects, that when small changes were made to them would get brand-new IDs. Various aspects about them would remain the same, and a field such as a name field would very often be very closely related, but not always. So, if we use people as an example, when I change Bob Smith's age from 30 to 31, an entirely new record is created for bob, with an entirely new ID, but all of the other data is closely related, or exactly the same. At the time of its creation, it wasn't important to keep track of if these were all the same Bob Smith or not, so the records are otherwise not related. Assume each record has perhaps 5-7 attributes to work with.
The most frequent case is that the object gets created once, and never has any modifications that cause a new record to be created. On the other extreme a single object may get say, 7 or 8 modifications that create new records, but not hundreds, possibly not even tens.
It has since become important to establish which of these records belong together, and for everything new created after a certain date, these associations were maintained... manually. By humans.
I've considered using something similar to a string distance function, but instead of characters making up a string, using attribute values making up a record to identify the 'distance' between records.
I'm curious though if the more recent records, where the association between these objects is tracked could be used as some kind of training set to train a model to create groups out of past data.
Obviously, my fictitious system doesn't make any sense, it's analogous to the real problem, and I'm trying to simplify it for consumption here.
I'm not even sure how to ask the question to begin searching for an answer or a method.