Mining Fuzzy Association Rules Using Mutual Information

Quantitative Association Rule (QAR) mining has been recognized as an influential research problem over the last decade due to the popularity of quantitative databases and the usefulness of association rules in real life. However, the combination of these quantitative attributes and their value intervals always rise to the generation of an explosively large number of itemsets, thereby severely degrading the mining efficiency. In this paper, we introduce a novel technique, called MFAMI, for mining quantitative association rules using fuzzy set theory. MFAMI employs linguistic terms to represent the revealed regularities and exceptions. This algorithm avoid the costly generation of a large number of candidate sets. Instead, using mutual information indicates the strong informative relationships among the attributes; potential frequent itemsets will be discovered. By utilizing those itemsets we devise an efficient algorithm that significantly generates rules. For effective mine rules, MFAMI employs adjusted difference analysis with this advantage that it does not require any user-supplied thresholds which are often hard to determine. Since the proposed algorithm greatly reduces the candidate subsequence generation efforts, the performance is improved significantly. Experiments show that the proposed algorithm is capable of discovering meaningful and useful fuzzy association rules in an effective manner, speeding up the mining process and obtaining most of the high confidence QARs.