Market basket analysis using association rules and its applications

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Published: Dec 16, 2024
Keywords:
Association rule Apriori Algorithm FP-growth Market Basket Analysis Recommended System

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Sirilux Kaewsirirung
Faculty of Science and Agricultural Technology Rajamangala University of Technology Lanna Nan Campus
Kanitha Homjun
Faculty of Science and Agricultural Technology Rajamangala University of Technology Lanna Nan Campus
Worawit Fankam-ai
Faculty of Science and Agricultural Technology Rajamangala University of Technology Lanna Nan Campus
Kannapat Polyiam
Faculty of Business and Liberal Art Rajamangala University of Technology Lanna Nan Campus
Jakkrit Muangsan
Faculty of Science and Agricultural Technology Rajamangala University of Technology Lanna Nan Campus
Warawut Wisomka
Faculty of Science and Agricultural Technology Rajamangala University of Technology Lanna Nan Campus
Nongnuch Ketui
Faculty of Science and Agricultural Technology, Rajamangala University of Technology Lanna Nan

Abstract

    Currently, many tourists in Thailand often discover a local product called One Tambon One Product (OTOP) during their travels. The problem is that tourists have difficulty determining which products are best-selling and suitable for use as their preferred item packages from the hundreds of available options. This is why association rule learning is needed to explore the correlation information and sales transaction patterns for OTOP items that are most frequently sold as product pairs. Our research aims to analyze the frequency of the most popular item sets from sales data in OTOP and to compare the performance of the Frequent Pattern Growth (FP-Growth) algorithm and the Apriori algorithm for OTOP recommendations. We used two datasets from Peanut House and the Nan OTOP Center, covering the years 2016 to 2022, with a total of 200,000 records for the experiment. This study aims to compare the performance of the Apriori and FP-Growth algorithms. The execution time of the Apriori algorithm outperformed that of the FP-Growth method. Overall, user satisfaction with the recommended system is rated very high, at 4.72.

Article Details

Issue
Vol. 43 No. 6 (2024)
Section
Original Articles

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