In this paper, a fuzzy association rule mining approach with type-2 membership functions is proposed for dealing with data uncertainty. It first transfers quantitative values in transactions into type-2 fuzzy values. Then, ac- cording to a predefined split number of points, they are reduced to type-1 fuzzy values. At last, the fuzzy association rules are derived by using these fuzzy val- ues. Experiments on a simulated dataset were made to show the effectiveness of the proposed approach.
['Chun-Hao Chen', 'Tzung-Pei Hong', 'Yu Li']
Fuzzy Association Rule Mining with Type-2 Membership Functions