<![CDATA[The growth of transaction data in the retail sector increases the need for analytical methods capable of identifying consumer purchasing patterns efficiently. This study applies the Apriori algorithm within the Cross Industry Standard Process for Data Mining (CRISP-DM) framework to perform market basket analysis on retail transaction data. The dataset consists of 934,974 transaction records, including 407,171 unique transactions and 27,344 unique products collected between July 2021 and September 2025. After the data cleaning process, 189,724 valid transactions were obtained. To improve computational efficiency, the analysis was limited to the 300 best-selling products, resulting in 90,718 transactions for the modeling stage. Frequent itemset generation was performed using a minimum support value of 0.1% and a maximum itemset length of three, producing 570 frequent itemsets consisting of 300 1-itemsets, 213 2-itemsets, and 57 3-itemsets. Association rule generation using a minimum confidence threshold of 70% and a lift ratio greater than 1 produced 52 valid rules. The best rule achieved a lift ratio of 161.32 and a confidence value of 93.88%, indicating a strong purchasing relationship among school supply products. The results demonstrate that the selected support and confidence parameters are effective in identifying meaningful purchasing patterns. Furthermore, the resulting association rules can support practical retail strategies, including product bundling, shelf arrangement optimization, and inventory management.]]>
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