PERFORMANCE EVALUATION OF DATA MINING ALGORITHMS AND MONTE CARLO SIMULATIONS FOR TREND DISCOVERY IN THE HAMBRE DELIVERY APP
DOI:
https://doi.org/10.47820/recima21.v6i8.6669Keywords:
Data Mining, Monte Carlo, Food Service, Knowledge Discovery, Association Rules, Sales TrendsAbstract
The food service sector is experiencing continuous growth, driven by the rise of online shopping. Online shopping streamlines transactions and improves the quality of products and services offered. Digitizing commercial management provides companies with the knowledge necessary to withstand health and economic crises, supported by specialized apps. While the adoption of AI-based tools has changed the way businesses operate, it poses a challenge for smaller, younger companies that do not yet offer online services. Motivated by the need to systematize the analysis of sales trends from partner stores of the Hambre Delivery app, this study proposes a computational performance evaluation solution that combines the Monte Carlo method and data mining algorithms to identify the most appropriate model for strategic sales management support. Through Monte Carlo simulations, the FP‑Growth, FP‑Max, Apriori, and Eclat algorithms were assessed in terms of scalability, execution time, and memory usage. The results showed that the Eclat algorithm is better suited to small, low-complexity data sets. FP-Growth and FP-Max, on the other hand, are scalable and can handle large volumes of data more efficiently in terms of execution time and memory usage. Additionally, the 27 generated association rules revealed relevant trends, showing that applying Monte Carlo results in more accurate and reliable patterns.
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