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2007
IEEE

Genetic Learning of Membership Functions for Mining Fuzzy Association Rules

9 years 1 months ago
Genetic Learning of Membership Functions for Mining Fuzzy Association Rules
— 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.
Rafael Alcalá, Jesús Alcalá-F
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where FUZZIEEE
Authors Rafael Alcalá, Jesús Alcalá-Fdez, María José Gacto, Francisco Herrera
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