Consumer Purchasing Pattern Analysis Using Market Basket Analysiswith FP-Growth Algorithm
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Abstract
This study aims to analyze product association patterns using the FP-Growth algorithm within the framework of Market Basket Analysis on retail transaction data. The dataset consists of 469 transactions involving the 31 most frequently purchased items recorded during the January–March 2020 period. The data were processed through preprocessing steps, including data cleaning and transformation using One-Hot Encoding, to enable analysis with the FP-Growth algorithm. The results identified 24 items that met the minimum support threshold, followed by the generation of association rules based on support and confidence parameters. Evaluation using the lift ratio indicated that most rules achieved values greater than 1, suggesting strong associations between products that are highly relevant for business strategies. The most dominant associations were found in the Indomie and Lifebuoy product categories, which can serve as the basis for cross-selling and bundling strategies. This research provides practical contributions for the retail sector by optimizing inventory management and data-driven promotion strategies. Future studies may expand the range of product variables and compare the FP-Growth algorithm with alternatives such as Apriori or Eclat.
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