<![CDATA[This study applies the Apriori method in data mining to analyze sales transaction data in building supply stores, aiming to identify consumer purchasing patterns that support strategic decision-making. The data mining process includes data cleaning, integration, selection, transformation, and the application of the Apriori algorithm to discover significant association rules. The analysis results reveal purchasing patterns, such as product combinations with confidence levels reaching 100%, indicating strong correlations between frequently co-purchased items. These findings are utilized to design strategies such as product bundling, optimizing item placement, and targeted promotions, significantly enhancing operational efficiency and customer satisfaction. This study demonstrates that the implementation of the Apriori algorithm is an effective solution for supporting data-driven management while strengthening the competitive edge of building supply stores in the retail industry.]]>
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