Preprocessing nominal data for frequent item set mining

This process will first create artificial data that can be compared to usual data loaded for frequent item set mining: Nominal Data with a true and false value, but differently mapped to internal indices. For ItemSet Mining these must be preprocessed to avoid problems: First they have to be transformed to Binominal Attributes, then it has to be defined, which is the positive and the negative value.