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Designing a Well-Structured E-Shop Using Association Rule Mining

机译:使用关联规则挖掘设计一家结构良好的电子店

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Many commercial companies collect large quantities of data from daily operations. For example, customer orders or purchase data are collected daily at the counters of grocery stores. Data mining is applied on such kind of data to extract patterns that could be useful to learn about the purchasing behavior of the customers. Such information are used to support a variety of business related tasks. For example, the investment of that kind of information in building a website for a grocery store. Association Rule Mining is one of the techniques used to mine databases. Association Rule Mining is the discovery of Association Rules showing attribute values that occur frequently together. In this paper, we have discovered Association Rules from a grocery store dataset which represents customer transactions in that grocery store. Those rules have been invested to design a well-structured website prototype for that grocery store. Promising results, that could affect the process of website design, have been found. The experiments showed that our method can reduce the cost up to 90% in some transactions.
机译:许多商业公司从日常运营中收集大量数据。例如,在杂货店的柜台上每天收集客户订单或购买数据。数据挖掘应用于此类数据以提取可能有用的模式,以了解客户的购买行为。这些信息用于支持各种业务相关任务。例如,在建立杂货店建立网站时这种信息的投资。关联规则挖掘是用于挖掘数据库的技术之一。关联规则挖掘是发现关联规则,显示频繁发生的属性值。在本文中,我们从一间杂货店的数据集,其代表在杂货店客户交易关联规则。这些规则已经投入为该杂货店设计一个结构良好的网站原型。已经发现了可能影响网站设计过程的有希望的结果。实验表明,我们的方法可以将成本降低到某些交易中的成本高达90%。

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