A binary PSO approach to mine high-utility itemsets

High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). Several algorithms have been presented tomine high-utility itemsets (HUIs) andmost of them have to handle the exponential search space for discoveringHUIswhen the number of distinct items and the size of database are very large. In the past, a heuristic HUPEumuGRAM algorithm was proposed to mine HUIs based on genetic algorithm (GA). For the evolutionary computation Communicated by V. Loia. B Jerry Chun-Wei Lin [email protected] Lu Yang [email protected] Philippe Fournier-Viger [email protected] Tzung-Pei Hong [email protected] Miroslav Voznak [email protected] 1 School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China 2 School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China 3 Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC 4 Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC 5 Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, Ostrava-Poruba, Czech Republic (EC) techniques of particle swarm optimization (PSO), it only requires fewer parameters compared to the GA-based approaches. Since the traditional PSO mechanism is used to handle the continuous problem, in this paper, the discrete PSO is adopted to encode the particles as the binary variables. An efficient PSO-based algorithm, namely HUIMBPSO, is proposed to efficiently find HUIs. The designed HUIM-BPSO algorithm finds the high-transaction-weighted utilization 1-itemsets (1-HTWUIs) as the size of the particles based on transaction-weighted utility (TWU) model, which can greatly reduce the combinational problem in evolution process. The sigmoid function is adopted in the updating process of the particles for the designed HUIM-BPSO algorithm. An OR/NOR-tree structure is further developed to reduce the invalid combinations for discovering HUIs. Substantial experiments on real-life datasets show that the proposed algorithm outperforms the other heuristic algorithms for mining HUIs in terms of execution time, number of discovered HUIs, and convergence.