I have the following task at hand:
Suppose, that there is a data on clients actions from the base stations of the mobile operator.
While being in the reach of the base station, client can make the following actions: Turn off the phone, change location, turn out of range, and etc. Hence, there are several data points for each phone number in the dataset, with attributes like coordinates of base station, type of the base station, type of device, client action, and so on. Also, there could be several base stations in one location, but pointing in different directions.
Also, there is a training set of phone numbers that belong to the same client. The task is to find all phone numbers that belong to one client (Each client can have 2 or 1 sim-cards).
As of now, my main idea is to somehow embed data points of each phone number into vector, and use some distance threshold small enough, to group these vectors into the pairs. But in this scenario i'm not using the training data at all.
Any suggestions would be appreciated.
P.S. After digging into the data a little bit, i found out that the data on each phone number is hugely varied in length. For instance, there could be 8 data points for one number, and 1000 for another. On this evidence, i'm not considering the idea with embedding viable anymore.