Consumer behavior plays a very important role in economics and targeted marketing. However, understanding economic consumer behavior is quite challenging, such as finding credible and reliable information on product profitability. Different from frequent pattern mining, utility-oriented mining integrates utility theory and data mining. Utility mining is a useful tool for understanding economic consumer behavior. Traditional algorithms for mining high-utility patterns (HUPs) applies a single/uniform minimum utility threshold ( minutil ) to obtain the set of HUPs, but in some real-life circumstances, some specific products may bring lower utilities compared with others, but their profit may offer some vital information. If minutil is set high, the patterns with low minutil are missed; if minutil is set low, the number of patterns becomes unmanageable. In this article, an efficient one-phase utility-oriented pattern mining algorithm, called HIMU, is proposed for mining HUPs with varied item-specific minimum utility. A novel tree structure called a multiple item utility set-enumeration tree (MIU-tree) and the global sorted and the conditional downward closure properties are introduced in HIMU. In addition, we extended the compact utility-list structure to keep the necessary information, and thus this one-phase HIMU model greatly reduces the computational costs and memory requirements. Moreover, two pruning strategies are then extended to enhance the performance. We conducted extensive experiments in several synthetic and real-world datasets; the results indicate that the designed one-phase HIMU algorithm can address the “ rare item problem ” and has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability. Furthermore, the enhanced algorithms outperform the non-optimized HIMU approach.

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消费者行为在经济学和定向营销中起着非常重要的作用。然而，了解消费者的经济行为是相当具有挑战性的，例如寻找关于产品盈利能力的可信和可靠的信息。与频繁模式挖掘不同，面向效用的挖掘融合了效用理论和数据挖掘。效用挖掘是了解经济消费者行为的有用工具。用于挖掘高效用模式 (HUP) 的传统算法应用单一/统一的最小效用阈值 (小工具) 来获取 HUP 集合，但在某些现实生活中，某些特定产品可能会带来比其他产品更低的效用，但它们的利润可能会提供一些重要信息。如果小工具设置为高，低模式小工具错过了；如果小工具设置得低，模式的数量变得难以管理。在本文中，提出了一种高效的单阶段效用导向模式挖掘算法，称为 HIMU，用于挖掘具有不同项目特定最小效用的 HUP。HIMU中引入了一种称为多项目效用集枚举树（MIU-tree）的新型树结构，以及全局排序和条件向下闭合属性。此外，我们扩展了紧凑的实用程序列表结构以保留必要的信息，因此这种单阶段 HIMU 模型大大降低了计算成本和内存需求。此外，随后扩展了两种修剪策略以提高性能。我们在几个合成和真实世界的数据集中进行了广泛的实验；结果表明，所设计的单相 HIMU 算法可以解决“稀有物品问题”，并且在运行时、内存使用和可扩展性方面比最先进的算法具有更好的性能。此外，增强的算法优于未优化的 HIMU 方法。