Design & Analysis of Fuzzy based Association Rule Mining

Data mining is sorting through data to identify patterns and establish relationships. Association rule mining is a well established method of data mining that identifies significant correlations between items in transactional data. Measures like support count, comprehensibility and interestingness, used for evaluating a rule can be thought of as different objectives of association rule mining problem. In this paper we proposed efficient fuzzy apriori association rule mining technique to find all co-occurrence relationships among data items. Our technique has three steps. Firstly, Transform the quantitative values of each transaction into fuzzy sets. calculate the membership values of each attribute in a transaction by applying the fuzzy membership function. In third step employs techniques for mining of fuzzy Apriori Associate rules. We also find fuzzy Apriori Association rule measured by Leverage. Our experimental results showed better performance than previous work.