Sales Transaction Data Analysis using Apriori Algorithm to Determine the Layout of the Goods

T. Hariguna, U. Hasanah, Nindi Nofi Susanti
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引用次数: 3

Abstract

In a shop, usually apply a sales strategy in order. The sales strategy can be in the form of determining the layout of goods so that they are close to one another. Determining the layout of items can be based on items that are often purchased simultaneously. Searching for items that are often purchased together can be done using data mining techniques, which is processing data to become more useful information. Sales transaction data processing can be done using apriori algorithm. Apriori algorithm is the most famous algorithm for finding high-frequency patterns and generating association rules. From the results of the discussion and data analysis, there were 3 (three) association rules formed, namely "If you buy Milo Active 18 grm, then buy ABC Kopi Susu 31G" with support 0.36% and 75% confidence, "If you buy Dancow 1 + Honey 200 grm, then buy Ice Cream Corneto" wit H Support 0.36% and confidence 60%, "If you buy SIIP Roasted 6.5 grm, then buy Davos Strong 10 grm" with support 0.36% and 75% confidence. From the association's rules can be used as decision making to determine the layout of goods that are likely to be purchased simultaneously by the buyer
用Apriori算法分析销售交易数据,确定商品布局
在商店里,通常按顺序应用销售策略。销售策略的形式可以是确定商品的布局,使它们彼此靠近。可以根据经常同时购买的物品来确定物品的布局。搜索经常一起购买的商品可以使用数据挖掘技术来完成,数据挖掘技术将数据处理成更有用的信息。销售交易数据的处理可以采用先验算法。Apriori算法是发现高频模式和生成关联规则的最著名算法。从讨论和数据分析的结果来看,形成了3(3)条关联规则,即“如果你买美洛Active 18克,那么买ABC Kopi Susu 31G”,支持度为0.36%,置信度为75%;“如果你买Dancow 1 + Honey 200克,那么买Ice Cream Corneto”,支持度为0.36%,置信度为60%;“如果你买SIIP Roasted 6.5克,那么买Davos Strong 10克”,支持度为0.36%,置信度为75%。从协会的规则可以作为决策,以确定商品的布局,可能会同时购买的买家
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