An Application Study of Association Rule Mining in Retail Market Basket Analysis

Abstract

In the contemporary retail landscape, characterized by intense competition and vast transactional data volumes, understanding customer purchasing behavior is paramount for strategic survival and growth. Market Basket Analysis (MBA) has emerged as a critical methodology for uncovering insights from these datasets, with Association Rule Mining (ARM) serving as its foundational data mining engine. This study conducts a comprehensive application study of ARM within the context of retail market basket analysis. The primary objective is to demonstrate the extraction and strategic application of association rules from large-scale transactional data to inform retail operations. This research utilizes an empirical approach, simulating the application of the Apriori algorithm on a substantial dataset representing retail transactions. The analysis focuses on the computation and interpretation of three key metrics: Support, Confidence, and Lift. The major findings reveal the identification of numerous high-support rules (indicating common purchases), high-confidence rules (useful for layout), and high-lift rules, which expose non-obvious yet statistically significant correlations between products, such as the relationship between diapers and beer, or gourmet coffee and imported cheese. These findings illustrate that relying solely on frequency or confidence metrics is insufficient; Lift provides the most actionable strategic insights by identifying genuine behavioral links rather than coincidental co-purchases driven by general popularity. This study concludes that the systematic application of ARM provides retailers with a robust, data-driven framework for optimizing store layout, developing targeted promotional bundling strategies, and refining inventory management, thereby bridging the gap between raw data analytics and tangible business value.