Optimizing Culinary Association Rules with Genetic Algorithms Using Lift and Novelty
DOI:
https://doi.org/10.30871/jaic.v10i2.12284Keywords:
Association Rule Mining, Genetic Algorithm, FP-Growth, Novelty Measurement, Transaction DataAbstract
The culinary industry generates large volumes of transaction data, yet conventional Association Rule Mining often produces excessive rules due to rule explosion. This issue can be addressed using Genetic Algorithms to optimize and select the most relevant rules. While the integration of ARM and Genetic Algorithms (GA) has been widely explored, most existing studies rely on the Apriori algorithm, which is computationally expensive and memory-intensive due to repeated database scans and candidate generation. Furthermore, many GA-based models prioritize confidence and novelty, often overlooking the lift metric, which may lead to rules lacking genuine positive correlation This study proposes an optimized framework that integrates FP-Growth with a Genetic Algorithm-driven evaluation based on lift and novelty to address these efficiency and quality gaps. The use of FP-Growth significantly enhances scalability by reducing memory consumption compared to traditional Apriori-based approaches. The main contribution of this research is a unified integration of FP-Growth and Genetic Algorithm with a multi-layered filtering mechanism enforcing minimum confidence and lift before optimization. This method effectively eliminates rule redundancy, reducing the redundancy rate from 6.06% in standard FP-Growth to 0%. Experiments on 36,164 transaction culinary records (Jan 2024–Sept 2025) demonstrate that the proposed FP-Growth+GA method achieves a 65.1% reduction in peak memory usage (50.68 MB) compared to Apriori+GA (145.54 MB). The method successfully prunes redundant patterns into 11 high-quality rules with lift values > 1 and novelty scores between 0.5 and 1.0, providing concise insights for menu bundling strategies. The implementation is available at https://bit.ly/github_ARM_with_GeneticAlgorithm.
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