零售商店植入式广告布局设计的购物篮分析

M. S, Sharmikha Sree R, K. Valarmathi
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引用次数: 0

摘要

人工智能利用信息挖掘和计算知识计算来进一步开发动态模型。市场购物篮分析是一种主要的关联规则学习策略,是一种信息挖掘策略,主要包括检查客户的市场购物篮中购买的物品的多少时间。在现有的系统中,使用优先级算法来查找频繁项集。但是,定位频繁使用的项集需要更长的时间,因为它必须反复扫描数据库,这是一个耗时的过程。提出的方法是为了解决现有方法的缺点。利用ECLAT算法将连续项目集从数据集中分离出来,然后制定隶属规则。
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Market Basket Analysis for Designing a Product Placement Layout in Retail Shop
AI use information mining and computational knowledge calculations to further develop dynamic models. Market Basket Analysis is one of the main affiliation rule learning is an information mining strategy, Consists of examining the much of the time bought thing in the market container of clients. In the existing system is use a Apriority algorithm is used for finding frequent item sets. However, it takes longer to locate frequently used item sets because it must repeatedly scan the database, which is a time-consuming procedure. The proposed method was created to address the shortcomings of the existing approach. The ECLAT algorithm is utilized to separate successive item sets from the data set, and afterward the affiliation rules are made.
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