High-utility itemsets mining based on binary particle swarm optimization with multiple adjustment strategies

Abstract

As an essential data mining task, high-utility itemset mining (HUIM) has attracted a lot of research. With the increase of dataset size, traditional exact HUIM algorithms are faced with exponential growth search space, which is unacceptable for some algorithms. As HUIM can be treated as a combinatorial optimization problem, Evolutionary Computation (EC) based HUIM approaches have been proposed and shown promise performance in mining HUIs. However, the existing EC-based HUIM approaches usually only find part of the HUIs in a limitation time or discovering all the HUIs is usually time-consuming. In this study, an improved binary particle swarm optimization for HUIM (HUIM-IBPSO) is proposed with multiple adjustment strategies to address these problems. In HUIM-IBPSO, a particle movement direction adjustment strategy is presented to keep the same HUIs during the evolution process. In order to utilize the repeated HUIs more efficiently and enhance the search ability, the strategy of local exploration is proposed in HUIM-IBPSO. A restart strategy for the population is developed in HUIM-IBPSO with the purpose to avoid the premature convergence before discovering any HUIs. To mine HUIs more efficiently, particle modify strategy and fitness value hash strategy are introduced in HUIM-IBPSO. A comprehensive comparison with five state-of-the-art EC-based HUIM algorithms and three precise HUIM algorithms on real datasets shows that the designed model outperforms them in terms of the number of HUIs found, the speed at which they converge, and the duration of execution (runtime).