An improved multi-support Apriori algorithm under the fuzzy item association condition

Apriori Algorithm has been successfully used in the mining of association rules. The algorithm with multiple-support is proposed for solving "rare item" problem, which means the common items and rare items cannot be satisfied within single support. However, the detailed identification of items will cause the poor mining rules, while the multi-level approach distinguishes the fuzzy association between items in real condition. This paper proposed a novel model based on tags to solve the problem. Each item has various tags, which implies the common attributes. This model is adaptive to the reality since certain item set can be accurately derived from whole sets using combination of tags by users as they like. A corresponding improved multi-support Apriori algorithm is also presented for a recommendation system. The experimental results demonstrated that the new model and the approach are more effective and adaptive.