Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the numberof items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed algorithms for finding high-utility itemsets in dynamically updated databases. Different from the batch processing algorithms that always process the databases from scratch, the incremental HUIM algorithms update and output high-utility itemsets in an incremental manner, thereby reducing the cost of finding high-utility itemsets. This paper provides the latest research on incremental high-utility itemset mining algorithms, including methods of storing itemsets and utilities based on tree, list, array and hash set storage structures. It also points out several important derivative algorithms and research challenges for incremental high-utility itemset mining.

中文翻译：

---

基于存储结构的增量式高效模式挖掘综述

传统的关联规则挖掘得到了广泛的研究，但这不适用于必须考虑物品的单位利润和购买数量等因素的实际应用。高效用项集挖掘（HUIM）旨在通过考虑购买的物品数量和单位利润来寻找高效用模式。然而，大多数高效的项集挖掘算法是为静态数据库设计的。在现实世界的应用程序（例如市场分析和业务决策）中，数据库通常通过动态插入新数据来更新。一些研究人员提出了在动态更新的数据库中查找高效项集的算法。与总是从头开始处理数据库的批处理算法不同，增量 HUIM 算法以增量方式更新和输出高效用项集，从而降低查找高效用项集的成本。本文提供了增量高效用项集挖掘算法的最新研究，包括基于树、列表、数组和哈希集存储结构的项集和效用存储方法。它还指出了增量高效项目集挖掘的几个重要的衍生算法和研究挑战。