1. A computer-implemented method for finding deviations in data, the method comprising: generating a set of candidate patterns; selecting from the set of candidate patterns a set of exception patterns that occur in the data less frequently than expected assuming statistical independence; processing data records that comply with at least one of the exception patterns as exception candidates; determining pattern-specific deviation degree values for the exception patterns; determining record-specific deviation degree values at least for the exception candidates based on the pattern-specific deviation degree values of exception patterns supported by respective exception candidates, wherein the pattern-specific deviation degree values are based on lift values of the respective exception patterns, the deviation degree values monotonously increasing with decreasing lift values; and selecting exceptions from the exception candidates based on the record-specific deviation degree values.