Mengungkap Pola Asosiasi Penjualan dengan Algoritma FP-Growth Data Mining
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Abstract
This study analyzes sales transaction data to identify product association patterns using the FP-Growth algorithm. A data mining approach is applied, including data preprocessing stages such as data selection, transformation, and binarization to ensure compatibility with the algorithm. Frequent itemsets are generated based on minimum support values and further processed to produce association rules evaluated using confidence and lift measures. The results demonstrate that the FP-Growth algorithm is able to efficiently discover meaningful association patterns without generating candidate itemsets, making it suitable for large transactional datasets. The identified rules reflect consistent customer purchasing behavior and reveal strong relationships among certain products. These findings can support business decision-making, particularly in developing marketing strategies, optimizing product placement, and improving inventory management. Overall, this study confirms that the FP-Growth algorithm is an effective method for extracting valuable knowledge from sales transaction data.
Keywords: Data Mining; FP-Growth; Association Rules; Sales Transactions; Frequent Itemsets.
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References
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