The High Average Utility Itemset Mining (HAUIM) technique, a variation of High Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most HAUIM algorithms were designed for static databases. However, practical applications like market basket analysis and business decision-making necessitate regular updates of the database with new transactions. As a result, researchers have developed incremental HAUIM (iHAUIM) algorithms to identify HAUIs in a dynamically updated database. Contrary to conventional methods that begin from scratch, the iHAUIM algorithm facilitates incremental changes and outputs, thereby reducing the cost of discovery. This paper provides a comprehensive review of the state-of-the-art iHAUIM algorithms, analyzing their unique characteristics and advantages. First, we explain the concept of iHAUIM, providing formulas and real-world examples for a more in-depth understanding. Subsequently, we categorize and discuss the key technologies used by varying types of iHAUIM algorithms, encompassing Apriori-based, Tree-based, and Utility-list-based techniques. Moreover, we conduct a critical analysis of each mining method's advantages and disadvantages. In conclusion, we explore potential future directions, research opportunities, and various extensions of the iHAUIM algorithm.

翻译：高平均效用项集挖掘（HAUIM）技术是高效用项集挖掘（HUIM）的一种变体，它利用项集的平均效用进行挖掘。历史上，大多数HAUIM算法是为静态数据库设计的。然而，诸如购物篮分析和商业决策等实际应用，通常需要定期向数据库中添加新交易记录以进行更新。因此，研究人员开发了增量式HAUIM（iHAUIM）算法，用于在动态更新的数据库中识别高平均效用项集。与从零开始的传统方法不同，iHAUIM算法支持增量式更新和输出，从而降低了挖掘成本。本文对最先进的iHAUIM算法进行了全面综述，分析了它们各自的特点和优势。首先，我们阐释了iHAUIM的概念，并通过公式和现实世界中的示例来加深理解。随后，我们对不同类型的iHAUIM算法所使用的关键技术进行了分类和讨论，涵盖了基于Apriori、基于树结构以及基于效用列表的技术。此外，我们对每种挖掘方法的优缺点进行了批判性分析。最后，我们探讨了iHAUIM算法未来可能的发展方向、研究机遇以及各种扩展应用。