In a quantitative sequential database, numerous efficient algorithms have been developed for high-utility sequential pattern mining (HUSPM). HUSPM establishes a relationship between frequency and significance in the real world and reflects more crucial information than frequent pattern mining. However, high average-utility sequential pattern mining (HAUSPM) is deemed fairer and more valuable than HUSPM. It provides a reasonable measure for longer patterns by considering their length. In contrast to scenarios in retail business analysis, some pattern mining applications, such as cybersecurity or artificial intelligence (AI), often involve much longer sequences. Consequently, pruning strategies can exert a more pronounced impact on efficiency. This paper proposes a novel algorithm named HAUSP-PG, which adopts two complementary strategies to independently process pattern prefixes and remaining sequences, thereby achieving a dual pruning effect. Additionally, the proposed method calculates average utility upper bounds without requiring item sorting, significantly reducing computational time and memory consumption compared to alternative approaches. Through experiments conducted on both real-life and synthetic datasets, we demonstrate that the proposed algorithm could achieve satisfactory performance.

翻译：在定量序列数据库中，已有众多高效算法被开发用于高效用序列模式挖掘（HUSPM）。HUSPM在现实世界中建立了频率与重要性之间的关系，相比频繁模式挖掘反映了更关键的信息。然而，高平均效用序列模式挖掘（HAUSPM）被认为比HUSPM更公平且更具价值。它通过考虑模式长度，为较长模式提供了合理的度量标准。与零售业务分析场景不同，某些模式挖掘应用（如网络安全或人工智能）常涉及更长的序列。因此，剪枝策略对效率的影响更为显著。本文提出了一种名为HAUSP-PG的新型算法，该算法采用两种互补策略分别处理模式前缀与剩余序列，从而实现双重剪枝效果。此外，所提方法在计算平均效用上界时无需进行项排序，相比现有方法显著减少了计算时间与内存消耗。通过在真实数据集与合成数据集上的实验，我们验证了所提算法能够取得令人满意的性能。