Grouping Goods with the Association Rule Method Using A priori Algorithms in Modern Retail Stores

  • Arif Rahmadsyah Faculty of Science and Technology, University of Pembangunan Panca Budi, Medan Indonesia
  • Nova Mayasari Faculty of Science and Technology, University of Pembangunan Panca Budi, Medan Indonesia
Keywords: Data mining, Association rule, AlgoritmaApriori, and Frequent Itemset.


The proliferation of retail companies in Indonesia makes competition quite fierce in this business. The availability of detailed information on products purchased from each customer transaction in the database will become data garbage, which continues to grow every day. Company managers will certainly be interested in knowing whether certain groups of products are consistently purchased together. They can use this data for optimal grouping and placement of products, and managers can also use this information for cross-selling, for promotions, for catalog design and to identify customer segments based on purchasing patterns. In this study, data mining with Association rule techniques was used to explore patterns of attribute relationships and frequent itemsets in the Retail database. The a priori paradigm is used to find large itemsets in the determination of association rules. The integration of association rules with the a priori paradigm has managed to find a number of patterns of relationships between attributes in retail databases.


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How to Cite
Rahmadsyah, A., & Mayasari, N. (2022). Grouping Goods with the Association Rule Method Using A priori Algorithms in Modern Retail Stores. International Journal of Science, Technology & Management, 3(6), 1527-1532.