Efficient Fuzzy Apriori Association Rule Mining to Find Co-occurance Relationship

Department of Computer Science & Engineering/Shriram College of Engineering & Management [SRCEM] Banmore, Gwalior (MP)/India, 474003 _______________________________________________________________________________________ Abstract: 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, the Apriori principle which allows considerably reduced the search space with discover the frequent item set. Secondly proposed an approach for finding fuzzy sets for quantitative attributes in a database by using k-mediod clustering techniques and finally employs techniques for mining of fuzzy Aprori Associate rules. We also find fuzzy Apriori Association rule measured by Leverage. Our experimental results showed better performance than previous work.