Genetic Learning of Membership Functions for Mining Fuzzy Association Rules

Abstract

Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consists of quantitative values. In the last years, the fuzzy set theory has been applied to data mining for finding interesting association rules in quantitative transactions. Recently, a new rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the 2-tuples linguistic representation model allowing us to adjust the context associated to the linguistic label membership functions. Based on the 2-tuples linguistic representation model, we present a new fuzzy data-mining algorithm for extracting both association rules and membership functions by means of an evolutionary learning of the membership functions, using a basic method for mining fuzzy association rules.