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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 them 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.

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