AN EXTENSION OF ASSOCIATION RULES USING FUZZY SETS Jee-Hyong Lee , Hyung Lee-Kwang

Abstract

. Association rules are a class of regularities existing between binary data tuples. This paper proposes an extension of association rules which can be applied to real-valued tuples. It discovers and describes association rules among real-valued tuples using fuzzy sets. The proposed method needs user-defined fuzzy sets for describing association rules. It extends the given tuples using the fuzzy sets and converts the extended tuples into binary tuples. Finally, it finds association rules by applying the existing algorithms for binary tuples to the converted binary tuples. Keywords: Data mining, Association rules, Fuzzy sets 1 Introduction Data mining is the technique which extracts the previously unknown and potentially useful information from large amount of data [1], [2]. Discovering association rules is one of the data mining techniques. Association rules give simple but strong knowledge on binary data tuples. They are the description that the tuples having a certain set of attrib..