双重剪枝与无排序的平均效用序列挖掘
Dual Pruning and Sorting-Free Overestimation for Average-Utility Sequential Pattern Mining
AI总结：
本文提出HAUSP-PG算法，通过双重剪枝策略和无排序计算平均效用上界，提升长序列挖掘效率。
摘要：
在定量序列数据库中，已开发出许多高效的高平均效用序列挖掘（HAUSPM）算法。HAUSPM在现实世界中建立了频率与重要性之间的关系，并比频繁模式挖掘提供了更多的关键信息。然而，高平均效用序列挖掘（HAUSPM）被认为比HAUSPM更公平且更有价值。它通过考虑序列长度来为更长的模式提供合理的度量。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（AI），往往涉及更长的序列。因此，剪枝策略对效率的影响更加显著。本文提出了一种名为HAUSP-PG的新算法，采用两种互补策略独立处理模式前缀和剩余序列，从而实现双重剪枝效果。此外，所提出的方法在不需项目排序的情况下计算平均效用上界，显著减少了计算时间和内存消耗，与替代方法相比。通过在真实生活和合成数据集上进行的实验，我们证明所提出的算法可以实现令人满意的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
