<![CDATA[This study analyzes user transaction patterns in e-commerce data using association rule mining. With the increasing volume of data, understanding consumer behavior is key to gaining a competitive advantage. Market basket analysis is employed to discover relationships between items that are frequently purchased together. The method involves several steps: data preprocessing of transaction records, followed by the application of an association algorithm like Apriori or FP-Growth to generate association rules. The strength of these rules is evaluated using metrics such as support, confidence, and lift. The results successfully identify significant purchasing patterns that can be used to improve business strategies. The insights gained from this analysis can be applied to personalize product recommendations, optimize website layouts, and design more effective product bundling promotions. Overall, this study demonstrates that association rule mining is a powerful tool for transforming transactional data into actionable business intelligence, ultimately increasing profitability and customer satisfaction in the e-commerce industry.]]>
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