Abstract: A data processing system processes data sets (such as low-resolution transaction data) into high-resolution data sets by mapping generic information into attribute-based specific information that is stored in a database. The extracting frequent pattern information from the database using frequent pattern growth techniques, a compact frequent pattern tree data structure efficiently holds frequent pattern information for multiple transactions having one or more items in each transaction. Frequent pattern data is transformed for ease of use with rule generation algorithms by removing redundant information (such as part group items) or by consolidating items corresponding to a part group and replacing those items with a proxy item for purposes of power set generation.