In the current world, information is one of the most important producing factors. So, trying to extract information from data is one of the challenges in the information industry and the related area. Volume of data is growing rapidly in all environments and in different ways. It shows the complexity of changing data to information. Data mining is one of the recent progresses in the field of data management. In the data mining, database theories, artificial intelligence, machine learning and statistics are combined to prepare an applied area. Data mining is composed of different methods, which one of the most important of them is association rules mining. The most currently used technique in association rules mining is Apriori algorithm. Various studies have been done already to develop association rules mining, which the Apriori algorithm has been a base for them. The focus of this research is on a new sight to the association rules mining according to the fuzzy logic. After proposing fuzzy logic, it is used in the intelligence systems, because of its similarity to the human reasoning and then it is entered to the data mining rapidly. In the compare with left; MARGIN: 0cm 0cm 0pt; unicode-bidi: embed; DIRECTION: ltr" align=left Keywords: Fuzzy association rules, minimum support, fuzzy taxonomy, itemset
