Managing high-variant product portfolios effectively is a crucial competitive advantage in offering mass customized products on saturated markets. Association Rule Mining (ARM) is a field of data mining determining frequent itemsets from historic transactions and deriving patterns of conclusion. This paper introduces a new approach to transfer ARM to feature-based configuration e.g. in the German automotive industry. Combined, existing apriori product knowledge is used in constraints to effectively lowering runtime by reducing the number of candidate-sets through introduction of a Boolean satisfiability check. For an efficient implementation, three different Apriori algorithms are tested and benchmarked on a generic dataset for different parameters. Results show a significant improvement in using SAT-based pre-screening while efficiency of the implementation depends on the given example.
