Hai et al. [21] applied double constraints to the problem.
Third, a structure and a unique representation of frequent itemsets with extended double constraints are shown by representative closed itemsets and their generators.
The main reason is that, when the extended double constraints changes, MFS-EDC executes without creating the lattice of closed itemset and their generators again from the dataset.
Let T_EDC, T_PP_EDC, and T_E_EDC be the average execution times of MFS-EDC, MFS-PP-EDC, and MFS-E-EDC for 100 selected extended double constraints.
Thus, using the lattice of closed itemsets and their generators which is also used in [2, 3, 15, 16, 17], we study and propose a new method to effectively mine frequent itemsets with above constraints, called extended double constraints (EDC).
We also propose an efficient algorithm MFS_EDC to exploit all frequent ones with extended double constraint.
The second one is to directly check all generated frequent itemsets on the constraints to filter frequent itemsets satisfying extended double constraint.
The first one is called MFS-E-EDC that is a new modified version of dEclat for mining frequent itemsets with the extended double constraint.
Based on the results, it is concluded that depending upon the constraint conditions, the double-constraint optimization problem can be simplified to solving either one of the single-constraint problems or directly finding the intersection points between the two constraint boundaries.
So, we can find that the extended double constraint presented in this paper is more general than that, ({mathcal F}{mathcal S}_{C_{10} subseteq C_{11}} ({s_0,shown), shown in [17], by extending a new kind of constraint set, (C_{21}), which is an arbitrary subset of ({mathcal A}).
