Applying Data Mining of Association Rules as Decision Making In Coffee-Shop: a Case Study

H. Elfira Febriani, R. Fitriana, Cendana Lestari Faturrahman
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Abstract

This study aims to transactions analysis from a coffee shop transaction in six months to define consumer purchases pattern with the development of a model of association rules. One of the coffee shops in Jakarta, the 8th Bean Cafe, was used as a study case in this study. The problems that occurred when deciding not based on data analysis, so they lost for 3 sales periods. This cafe has 80 menus that keep changing every time according to the owner’s wishes without knowing the favorite menu, the most frequently purchased menu, and so on. They never analyze what menu that consumers are interested in buying. Based on the results found 3 association rules, namely {CLASSIC $\Rightarrow$ SIGNATURE}, {FRIED RICE AND PASTA $\Rightarrow$ LIGHT BITES}, {LIGHT BITES FRIED RICE AND PASTA} from sales transaction data mining. The rules provide information that two types of 2-itemset combinations tend to buy together.
关联规则数据挖掘在咖啡店决策中的应用:一个案例研究
本研究旨在通过对某咖啡店6个月的交易进行分析,通过建立关联规则模型来定义消费者的购买模式。在本研究中,雅加达的一家咖啡店,第八豆咖啡馆,被用作研究案例。在决策时出现的问题不是基于数据分析,所以他们输了3个销售周期。这家咖啡馆有80个菜单,每次都根据主人的意愿不断改变,不知道最喜欢的菜单,最常购买的菜单等等。他们从不分析消费者对什么菜单感兴趣。基于结果从销售交易数据挖掘中发现了3条关联规则,即{CLASSIC $\Rightarrow$ SIGNATURE}、{FRIED RICE AND PASTA $\Rightarrow$ LIGHT BITES}、{LIGHT BITES FRIED RICE AND PASTA}。规则提供了两种类型的2-itemset组合倾向于一起购买的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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