I have a list of accounts as data set and I need to group the accounts that refer to the same user using many features.
I'm thinking to use machine learning( but I'm new in this domain), because I know the group of each account for the training data set.
example of training data:
account-id Feature1 Feature2 class(Group) 1 T1 P4 Gr1 2 T2 P4 Gr1 3 T3 P2 Gr2
The problem is in the testing of data and when a new account arrive for a new group not learned before in the training set.
example of testing data:
account-id Feature1 Feature2 4 T5 P5 5 T6 P5 6 T3 P2
The groups of the testing data should be as following:
account-id Feature1 Feature2 class(Group) 4 T5 P5 Gr3 5 T6 P5 Gr3 6 T3 P2 Gr2
The accounts 4 and 5 are in a new group (Gr3) which is not learned before in the training data.
My question is how could I group the new data under a new class that is not defined before in the learning phase ? and which algorithm can I use to solve this issue ?