<![CDATA[This study explores the optimization of sales and inventory management for electronic products in e-commerce platforms by applying the FP-Growth algorithm within data mining techniques. Amid the rise of digital transactions, many businesses still struggle to uncover hidden patterns in consumer purchasing behavior. This research analyzes 163 transaction records collected through questionnaires, with 30 transactions selected as the primary sample. The FP-Growth method was chosen for its efficiency in identifying frequent itemsets without generating candidate sets, making it suitable for large-scale data analysis. Significant findings from this study reveal strong associations between certain products, such as the itemset “Refrigerator is frequently followed by Microwave” with a support value of 23% and a confidence of 92%, as well as “Washing Machine is often purchased together with Iron” with 43% support and 100% confidence. These results indicate that consumers tend to purchase these items together. Understanding these patterns allows businesses to design more targeted bundling and promotional strategies, while also improving stock management. This research offers practical insights for data-driven decision-making, ultimately enhancing sales performance and operational efficiency in the e-commerce sector.]]>
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