An effective Boolean algorithm for mining association rules in large databases

In this paper, we present in effective Boolean algorithm for mining association rules in large databases of sales transactions. Like the a priori algorithm, the proposed Boolean algorithm mines association rules in two steps. In the first step, logic OR and AND operations are used to compute frequent itemsets. In the second step, logic AND and XOR operations are applied to derive all interesting association rules based on the computed frequent itemsets. By only scanning the database once and avoiding generating candidate itemsets in computing frequent itemsets, the Boolean algorithm gains a significant performance improvement over the a priori algorithm. We propose two efficient implementations of the Boolean algorithm, the bitstream approach and the sparse-matrix approach. Through comprehensive experiments, we show that both the bitstream approach and the sparse-matrix approach outperform the a priori algorithm in all database settings. The sparse-matrix approach in particular shows a very significant performance improvement over the a priori algorithm.