Amir Ebrahimzadeh, Afro Asian J SciTech,2014,1(1), 26-31

© 2014. The Authors. Published under Afro Asian Journal of Science and Technology www.aajst.com 26 Amir Ebrahimzadeh Sama technical and vocational training college, Islamic Azad University, Mashhad branch, Mashhad, Iran Abstract due to increasing use of huge databases , mining practical information and useful knowledge from transactions is evolving into an important area. Most data mining methods focuses on relationship among transactions. Many algorithms have been proposed to find association rules in databases with either binary or quantitative attributes. One of these approaches is fuzzy association rules mining. Fuzzy Apriori and its different variations are the popular fuzzy association rule mining (ARM) algorithms available today. Like the crisp version of Apriori, fuzzy Apriori is a very slow and inefficient algorithm for very large datasets. Hence ,in this paper, we introduce a novel technique , called FCT, for mining fuzzy association rules . Existing method discovers fuzzy association rules by scaning the database once, and performing three tasks simultaneously .First , compute the fuzzy supports of candidate 1itemsets and then generate large 1-itemsets.Second , divide database into multiple cluster tables ,such that transaction with length k , fall into cluster table k.