<![CDATA[Competition in the business world, particularly in the automotive spare parts sales industry, is becoming increasingly intense, requiring businesses to develop marketing strategies to increase sales. The growing volume of daily sales data can be utilized to develop effective marketing strategies when properly processed and analyzed. Sales of automotive spare parts increase every month, with a wide variety of products offered to attract customers. To identify the products with the highest sales, the Apriori algorithm can be applied with the assistance of the Tanagra software. This approach enables businesses to identify the best-selling products based on sales transaction data. The Apriori algorithm is a type of association rule in data mining. One of the key stages in association analysis that has attracted considerable research interest is frequent pattern mining, which aims to generate efficient algorithms for identifying frequently occurring itemsets. The significance of an association rule can be determined using two main measures: support and confidence. Support represents the percentage of transactions in the database containing a particular item combination, while confidence measures the strength of the relationship between items in an association rule. By applying the Apriori algorithm, businesses can identify purchasing patterns and develop more effective marketing strategies to improve sales performance]]>
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