CONCISE: An Algorithm for Mining Positive and Negative Non-Redundant Association Rules

One challenge problem in association rules mining is the huge size of the extracted rule set many of which are uninteresting and redundant. In this paper, we propose an efficient algorithm CONCISE for generating all non-redundant positive and negative association rules. We introduce GC2M algorithm for enumerating simultaneously all frequent generator itemsets, frequent closed itemsets, frequent maximal itemsets, and infrequent minimal itemsets. We then define four new bases representing non-redundant association rules. We prove that these bases significantly reduce the number of extracted rules. We show the efficiency of our algorithm by computational experiments compared with existing algorithms.