Mining Generalized Fuzzy Association Rules via Determining Minimum Supports

Association rule mining is based on the assumption that users can specify the minimum-support for mining their databases. It has been identified that setting the minimum support is a difficult task to users. This can hamper the widespread applications of these algorithms. This paper proposes a method for computing minimum supports for each item. It therefore will run the fuzzy multi-level mining algorithm for extracting knowledge implicit in quantitative transactions, immediately. More specifically, our algorithms automatically generate actual minimum-supports according to users’ mining requirements. In order to address this need, the new approach can express tow profits includes computing the minimumsupport for each item regarding to characteristic for each item in database and making a system automation. We considered an algorithm that can cover the multiple level association rulesunder multiple item supports. We experimentally examine the algorithms using a dataset, and demonstrate that our algorithm fittingly approximates actual minimum-supports from the commonly-used requirements

Keywords:

Fuzzy data mining , minimum confidence , multiple minimum supports , membership functions , association rule