Comparative Analysis of Association Rule Mining Algorithms Using Apriori and Eclat on Retail Transaction Data

Retail transaction data can be utilized to identify consumer purchasing patterns and product relationships. This study evaluates and compares the performance of the Apriori and ECLAT algorithms in generating association rules from a retail dataset comprising 30,000 transactions collected between January and April 2025. A grid search method was employed to determine the optimal minimum support and minimum confidence thresholds. Using a minimum support of 0.005 and a minimum confidence of 0.3, both algorithms generated 1,736 frequent itemsets and 78 association rules. The resulting rules were assessed using support, confidence, and lift measures, and all rules obtained lift values greater than 1, indicating meaningful positive dependencies among products. The strongest rule revealed that customers who purchased Bread and Milk were highly likely to also purchase Cereal, with a confidence of 60.5% and a lift value of 2.70. Although both algorithms produced comparable rule quality, ECLAT demonstrated superior computational efficiency due to its vertical data representation approach. These findings provide practical insights for retail decision-making, particularly in cross-selling strategies, product bundling, and shelf arrangement optimization.