Despite advances in algorithmic design, association rule mining remains problematic from a performance viewpoint when the size of the underlying transaction database is large. The well-known a priori approach, while reducing the computational effort involved still suffers from the problem of scalability due to its reliance on generating candidate itemsets. In this paper we present a novel approach that combines the power of preprocessing with the application of user-defined constraints to prune the itemset space prior to building a compact FP-tree. Experimentation shows that that our algorithm significantly outperforms the current state of the art algorithm, FP-bonsai.