<![CDATA[The increasing volume and complexity of sales transaction data in the digital era have prompted companies and organizations to capitalize on the valuable information it holds. Understanding purchase patterns in sales transaction data is critical for discerning product associations and consumer behavior, thus optimizing marketing strategies and data-driven decision-making. This study concentrates on assessing the performance of the Apriori algorithm, a popular association analysis technique, in clustering sales transaction data to uncover purchase patterns. Using sales transaction data from retail stores, which includes customer identities and purchased products, the Apriori algorithm identifies frequent itemsets that represent common purchase patterns. The results of the purchase pattern analysis and product associations offer valuable insights for companies to fine-tune marketing strategies and enhance the overall customer experience. The research demonstrates that the Apriori algorithm effectively identifies frequent purchase patterns and product associations in sales transaction data. The algorithm's efficiency makes it suitable for analyzing retail sales data effectively. This research contributes to understanding the Apriori algorithm's performance in analyzing sales transaction data for purchase pattern analysis, empowering businesses to make informed decisions based on product associations and customer preferences.]]>
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