Mining significant fuzzy association rules with differential evolution algorithm

Abstract

This article presents a new differential evolution (DE) algorithm for mining optimized statistically significant fuzzy association rules that are abundant in number and high in rule interestingness measure (RIM) values, with strict control over the risk of spurious rules. The risk control over spurious rules, as the most distinctive feature of the proposed DE compared with existing evolutionary algorithms (EAs) for association rule mining (ARM), is realized via two new statistically sound significance tests on the rules. The two tests, in the experimentwise and generationwise adjustment approach, can respectively limit the familywise error rate (the probability that any spurious rules occur in the ARM result) and percentage of spurious rules upon the user specified level. Experiments on variously sized data show that the proposed DE can keep the risk of spurious rules well below the user specified level, which is beyond the ability of existing EA-based ARM. The new method also carries forward the advantages of EA-based ARM and distinctive merits of DE in optimizing the rules: it can obtain several times as many rules and as high RIM values as conventional non-evolutionary ARM, and even more informative rules and better RIM values than genetic-algorithm-based ARM. Case studies on hotel room price determinants and wildfire risk factors demonstrate the practical usefulness of the proposed DE.