Hybrid Dimension Mining by Fuzzy Association Rule

Mining hybrid dimension fuzzy association rule is one of the important processes in data mining . Apriori algorithm concerned with handling single level, single dimensional association rules. this paper is presenting, a new modification in joining process to reduce the redundant generation of sub items during pruning the candidate itemsets, which can obtain higher efficiency of mining that of original algorithm when the dimension of data is high. Hybrid dimensional association rules are obtaining by using improved joining method of Apriori Algorithm. Support and confidence are the two most important quality measures for evaluating the interestingness of association rules. Crisp set have a well defined universe of members .Crisps sets allow only full membership or no membership at all, where as fuzzy sets allow partial membership. In fuzzy data there are three values namely 0 indicating that an attribute is not a member of the set, values between 0 & 1 indicating that an attribute is partially a member of the set involving partial membership. It means that an attribute is definitely a member of the set involving full membership. This Approach can be characterized by generating hybrid dimensional association rules mining as a generalization of inter-dimension and intra-dimension rule. In this project, two measures support and confidence are calculated for hybrid dimensional association rules by using fuzzy concept. Fuzzy concept is used to acquire an accurate hybrid dimension association rule by calculating Support and Confidence.