Data Mining on Sales Transaction Data Using the Association Method with Apriori Algorithm

Abstract

The purpose of this study was to determine consumer buying patterns at CV. XYZ by utilizing one of the data mining methods, namely the association method. This apriori algorithm belongs to the type of data mining rules. In other words, store owners can manage product placement and design marketing campaigns to determine the association rules between item combinations and determine the results of the association rules from consumer purchase analysis. The apriori algorithm is tasked with finding the frequent itemset or the itemset with the most frequent occurrence of all sales transactions so that association rules can be formed with the help of the RapidMiner application. Thus, all the sales transaction data in the company can be reprocessed to obtain critical information. Sales transaction data will be processed using Knowledge Discovery in Database (KDD). The test results with the RapidMiner application get four association rules. The best association rule is that if consumers buy Pants with code 1076, they are also likely to purchase Pants with code 0814 (confidence = 83.3% & lift = 18.5).