Sales data analysis and product layout analysis model based on association rule mining algorithm

This study uses association rule mining algorithms to conduct in-depth analysis of sales data of retail enterprises, aiming to reveal the purchasing patterns and correlations between products. By analyzing the transaction records of supermarkets, this study identified multiple frequently purchased product combinations and proposed targeted product layout strategies based on these findings. These strategies include neighboring placement of interrelated products, bundled promotions, and inventory management optimization. The preliminary results after implementation indicate that these data-driven layouts and promotional strategies have effectively improved sales and customer satisfaction. This study not only demonstrates the practical application of association rule mining in the retail industry, but also provides a new perspective for retail enterprises to utilize sales data and optimize business operations. Finally, this article also discusses the limitations of the research and proposes possible directions for future research.