Abstract
Marketing efforts and store layout could benefit from studying purchases that commonly happen together. This type of studies are commonly referred to as market basket analysis (MBA). In this work, a market basket methodology based on minimum spanning trees (MST) is presented. Because of the wide variety of products in a typical grocery store, and the heterogeneity of consumer shopping behavior, MBA is a complex task, from a computational point of view, and for subsequent interpretations of the results. The proposed methodology simplifies significantly the process of finding sets of products that have high co-occurrence in the market basket of the consumers, that is, products that are bought together. The resulting MST as a visual representation that connects all products with a high correlation to each other is easy to interpret and becomes a powerful tool to propose marketing actions. This solution turns out to be a complement with the traditional association rules used for MBA.
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This work was supported by Fondecyt Research Scholarship (Chile), grant No: 11160072.
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Valle, M.A., Ruz, G.A., Morrás, R. (2018). Market Basket Analysis Using Minimum Spanning Trees. In: Alhajj, R., Hoppe, H., Hecking, T., Bródka, P., Kazienko, P. (eds) Network Intelligence Meets User Centered Social Media Networks. ENIC 2017. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-90312-5_11
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