Discovering Frequent Itemset with Maximum Time-Window on Temporal Transaction Database using Variable Neighborhood Search

In this paper, we studied the problem of maximum frequent time-window selection (MFTWS) that appears in the process of discovering frequent itemset with time-windows (FITW). We formulated this problem as an integer programming mathematical model that is a typical combination problem with a solution space exponentially related to the problem size. A variable neighborhood search algorithm has been developed to solve the problem with near-optimal solutions. Computational experiments have been carried out to test the performances of the VNS algorithm against benchmark problem set. The results show that the VNS algorithm is an effective approach for solving the MTFWS problem, capable of discovering quite many FITWs with larger time-coverage rate than lower bounds, and lay a base for future's further studies on this problem.