Classifying Association Rules with Minimized Sets using Fuzzy-Aprioi Algorithm

Using Association Rule mining is extremely well-organized method for getting strong relation between correlated data or information. The correlation of data provides significance complete taking out progression. For the mining of positive and negative rules, a range of algorithms are utilized for example Apriori algorithm and tree based algorithm. A numeral of algorithms is be unsure presentation but manufactures huge number of negative association rule and also goes through from multi-scan difficulty. The proposal of is to get rid of these difficulties and decrease huge amount of negative rules. Here an efficient technique is implemented for the classification of association rules generated using Fuzzy-Apriori algorithm and classification of these rules can be done supervised learning such as Naive Bayes Algorithm. The proposed methodology implemented here provides efficient results as compared to the existing technique implemented for the generation of association rules.