Abstract

The initiative of combining association rule mining with fuzzy set theory has been applied frequently in recent years [1-5] The original idea conies horn cleating with quantitative attributes in a database, where discretization of the quantitative values into intervals would leach to (lintel or overestimation of the values that are neat the borders This is called the sharp boundary problem Fuzzy sets can help us to overcome this problem by allowing different degrees of the membership. not only I and 0 treated by traditional methods Attribute values can thereby be the membeis of mote than one set mid therefore give a more realistic view, on such data On the whet hand. fuzzy set theory has been shown to be a very useful tool in association rule mining. because the mined rules can be expressed in linguistic terms. winch ate more mutual and understandable lot human beings The linguistic representation is mainly useful when those discovered rules are presented to human experts for study In this impel a novel association rule mining approach that integrates the evolutionary optimization technique 'genetic network programming (GNP). and fuzzy set theory has been proposed lot mining interesting fuzzy rules from given quantitative dam The performance of our algorithm has been compared with other relevant algorithms and the experimental results show the advantages and effectiveness of the proposed model 2010 Institute of Electrical Engineers of Japan Published by John Wiley Sons. Inc