Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.
School of Computer Science and Engineering, International University-VNU-HCMC, Ho Chi Minh City 700000, Vietnam.
Sensors (Basel). 2020 Feb 17;20(4):1078. doi: 10.3390/s20041078.
In business, managers may use the association information among products to define promotion and competitive strategies. The mining of high-utility association rules (HARs) from high-utility itemsets enables users to select their own weights for rules, based either on the utility or confidence values. This approach also provides more information, which can help managers to make better decisions. Some efficient methods for mining HARs have been developed in recent years. However, in some decision-support systems, users only need to mine a smallest set of HARs for efficient use. Therefore, this paper proposes a method for the efficient mining of non-redundant high-utility association rules (NR-HARs). We first build a semi-lattice of mined high-utility itemsets, and then identify closed and generator itemsets within this. Following this, an efficient algorithm is developed for generating rules from the built lattice. This new approach was verified on different types of datasets to demonstrate that it has a faster runtime and does not require more memory than existing methods. The proposed algorithm can be integrated with a variety of applications and would combine well with external systems, such as the Internet of Things (IoT) and distributed computer systems. Many companies have been applying IoT and such computing systems into their business activities, monitoring data or decision-making. The data can be sent into the system continuously through the IoT or any other information system. Selecting an appropriate and fast approach helps management to visualize customer needs as well as make more timely decisions on business strategy.
在商业中，经理们可能会利用产品之间的关联信息来定义促销和竞争策略。从高效用项目集中挖掘高效用关联规则（HARs），使用户可以根据效用或置信度值为规则选择自己的权重。这种方法还提供了更多的信息，可以帮助管理者做出更好的决策。近年来，已经开发出了一些挖掘 HARs 的有效方法。然而，在某些决策支持系统中，用户只需要挖掘一组最小的 HARs 以实现高效利用。因此，本文提出了一种高效挖掘非冗余高效用关联规则（NR-HARs）的方法。我们首先构建了一个挖掘出的高效用项目集的半格，然后在这个半格中识别封闭和生成项目集。在此基础上，开发了一种从构建的格中生成规则的有效算法。该新方法在不同类型的数据集上进行了验证，结果表明其运行时间更快，所需内存比现有方法更少。所提出的算法可以与各种应用程序集成，并与外部系统（如物联网（IoT）和分布式计算机系统）很好地结合。许多公司已经将物联网和此类计算系统应用于其业务活动中，以监控数据或做出决策。数据可以通过物联网或任何其他信息系统连续发送到系统中。选择一个合适且快速的方法有助于管理层了解客户需求，并对业务战略做出更及时的决策。
