[[abstract]]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, according
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 values.
Experiments on a simulated dataset were made to show the effectiveness of
the proposed approach.[[notice]]補正完
