I have a big dataset with a column "clientid" and a categorical column "choice". I want to find out what are the clients that have strange combinations of choices (less frequent ones) and being able in the future to identify themnew strange combinations of future clients immediately.
| clientid | choice |
|---|---|
| cl1 | a |
| cl2 | b |
| cl2 | c |
| cl3 | d |
| cl4 | b |
| cl4 | c |
If I transpose the table by clientID I have a row for each client and different columns based on the choices, it will became a sparse dataset with categorical variables (choices). Some clients have only one choice and some have multiple ones and I want to find outlier records (clientid)
Which type of algorithm could help me in this type of problem? It is unsupervised, so I dont know what are the normal combinations and it is sparse data on categorical variables.