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

Abstract

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 first introduce an algorithm CMG (Closed, Maximal and Generators) for mining all frequent closed, maximal and their generators itemsets from large transaction databases. We then define four new bases representing non-redundant association rules from these frequent itemsets. 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.