Association Rule Mining for Market Basket Analysis in Retail Data: Enhancing Automated Knowledge Discovery with Apriori and FP-Growth Algorithms

Abstract

Market basket analysis is a cornerstone of retail analytics, providing strategic insights into consumer purchasing behavior through the discovery of item associations within large transactional datasets. By identifying frequent itemsets and generating association rules, retailers can optimize store layouts, tailor promotional campaigns, and refine product assortments to drive sales. Apriori and FP-Growth algorithms serve as fundamental techniques in this domain, each employing distinct data structures and pruning strategies to manage extensive candidate spaces while maintaining computational efficiency. Apriori utilizes a level-wise approach that systematically generates and prunes candidate itemsets by leveraging the anti-monotonic property, whereas FP-Growth exploits a tree-based representation to compress item occurrences and reduce redundant database scans. Both methods achieve high-performance pattern extraction even as datasets scale to millions of transactions. In an era increasingly defined by omnichannel retail, real-time analytics, and vast item catalogs, the ability to uncover hidden patterns rapidly is crucial for competitive advantage. Beyond classical retail, these mining methods find applications in areas such as bioinformatics, fraud detection, and content recommendation, where co-occurrence relationships can reveal critical insights. This paper presents an extensive discussion on the theoretical underpinnings, methodological frameworks, and practical considerations of association rule mining, with a focus on automated knowledge discovery enhancements that leverage Apriori and FP-Growth in complex, data-rich environments.