Summary: After the current definition of the inverse frequent set mining problem is expanded and its three practical applications are explored, an FP-tree-based method is proposed for the inverse mining problem. First, the method divides target constraints into some sub constraints and each time it solves a sub linear constraint problem. After some iterations, it finds an FP-tree satisfying the whole given constraints. Then, based on the FP-tree it generates a temporary database \(TempD\) that only involves frequent items. The target datasets are obtained by scattering infrequent items into \(TempD\). Theoretic analysis and experiments show that the method is right and efficient. Moreover, compared with the current methods, the method can output more than one target data set.