An efficient PSO-based evolutionary model for closed high-utility itemset mining

Abstract

High-utility itemset mining (HUIM) is a widely adopted data mining technique for discovering valuable patterns in transactional databases. Although HUIM can provide useful knowledge in various types of data, it can be challenging to interpret the results when many patterns are found. To alleviate this, closed high-utility itemset mining (CHUIM) has been suggested, which provides users with a more concise and meaningful set of solutions. However, CHUIM is a computationally demanding task, and current approaches can require prolonged runtimes. This paper aims to solve this problem and proposes a meta-heuristic model based on particle swarm optimization (PSO) to discover CHUIs, called CHUI-PSO. Moreover, the algorithm incorporates several new strategies to reduce the computational cost associated with similar existing techniques. First, we introduce Extended TWU pruning (ETP), which aims to decrease the number of possible candidates to improve the discovery of solutions in large search spaces. Second, we propose two new utility upper bounds, used to estimate itemset utilities and bypass expensive candidate evaluations. Finally, to increase population diversity and prevent redundant computations, we suggest a structure called ExploredSet to maintain and utilize the evaluated candidates. Extensive experimental results show that CHUI-PSO outperforms the current state-of-the-art algorithms regarding execution time, accuracy, and convergence.