基于Apriori算法的客户产品推荐数据挖掘

Ariefana Ria Riszky, M. Sadikin
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引用次数: 39

摘要

营销策略的实施需要一个参考,以便推广能够达到目标,例如通过寻找产品项目之间的相似性。本研究考察了关联规则方法和apriori算法在采购交易数据集中的应用,以帮助形成客户推荐产品促销的产品项目之间的候选组合。采购交易数据集收集于2018年10月和11月,总数据为1027。在实验中,通过使用Weka软件3.9版本处理数据,支持度的最小值为85%,最小置信度值为90%。Apriori算法可以根据定义的最小支持度和置信度值,形成关联规则,作为公司产品推广的参考,以及为客户提供产品推荐的决策支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan
The implementation of a marketing strategy requires a reference so that promotion can be on target, such as by looking for similarities between product items. This study examines the application of the association rule method and apriori algorithm to the purchase transaction dataset to assist in forming candidate combinations among product items for customer recommended product promotion. The purchase transaction dataset was collected in October and November 2018 with a total data of 1027. In the experiment, the minimum value of support is 85%, and the minimum confidence value is 90% by processing data using the Weka software 3.9 version. Apriori algorithm can form association rules as a reference in the promotion of company products and decision support in providing product recommendations to customers based on defined minimum support and confidence values.
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