High Utility Item-set Mining (HUIM) is the futuristic remodel version of Frequent Item-set Mining (FIM). It discovers customer purchase trends in the retail market. This knowledge is useful to retailers to incorporate various innovative schemes in their businesses to attract the customers such as discounts, cross-marketing, seasonal sale offers…etc. Even though many HUIM algorithms are available to detect profitable patterns, most of them cannot apply to all kinds of retail market data sets due to certain assumptions. The first assumption is that the items always produce a positive profit. Even though purchased items’ overall profit could be positive, few items may have negative profit. Another assumption is they are built for static transactional data. The data is gathered up to the point of time and is used for analysis. It is helpful to make decisions at some intervals like quarterly, half-yearly, yearly. But, to take decisions at any time by analyzing the present sales trend, it is required to process the data stream. This paper presents an innovative idea named Extended Global Utility Item-sets Tree(EGUI-tree) to extract High utility item-sets in the retail market data stream with positive and negative profit items. The sliding window-based technique is applied to the data stream to pick up the very recent data to process. An experimental study on real-world datasets shows that the proposed EGUI-tree algorithm is faster and scalable.

高效项目集挖掘 (HUIM) 是频繁项目集挖掘 (FIM) 的未来改造版本。它发现零售市场中的客户购买趋势。这些知识有助于零售商在其业务中采用各种创新计划来吸引客户，例如折扣、交叉营销、季节性销售优惠等。尽管有许多 HUIM 算法可用于检测盈利模式，但由于某些假设，其中大多数算法无法适用于所有类型的零售市场数据集。第一个假设是这些项目总是产生正利润。尽管购买的商品的整体利润可能是正的，但很少有商品会出现负利润。另一个假设是它们是为静态事务数据而构建的。收集到时间点的数据并用于分析。每隔一段时间（例如每季度、每半年、每年）做出决定是有帮助的。但是，要通过分析当前的销售趋势随时做出决策，就需要对数据流进行处理。本文提出了一种名为扩展全局效用项集树（EGUI-tree）的创新思想，用于在零售市场数据流中提取具有正负利润项的高效用项集。基于滑动窗口的技术应用于数据流以选取最近的数据进行处理。对真实世界数据集的实验研究表明，所提出的 EGUI 树算法速度更快且可扩展。本文提出了一种名为扩展全局效用项集树（EGUI-tree）的创新思想，用于在零售市场数据流中提取具有正负利润项的高效用项集。基于滑动窗口的技术应用于数据流以选取最近的数据进行处理。对真实世界数据集的实验研究表明，所提出的 EGUI 树算法速度更快且可扩展。本文提出了一种名为扩展全局效用项集树（EGUI-tree）的创新思想，用于在零售市场数据流中提取具有正负利润项的高效用项集。基于滑动窗口的技术应用于数据流以选取最近的数据进行处理。对真实世界数据集的实验研究表明，所提出的 EGUI-tree 算法速度更快且可扩展。