Abstract
In this paper we present a proposal to analyze market baskets using minimum spanning trees, based on couplings between products. The couplings are the result of a learning process with Boltzmann machines from transactional databases, in which the interaction between the different offers of the market are modeled as a network composed by magnetic dipoles of spins that can be in two states (\(+\)1 or −1). The results offer a systematic way to explore potential courses of action to determine promotions and offers for the retail manager.
The authors would like to thank CONICYT-Chile under grant Fondecyt 11160072 (M.A.V.) and Basal (CONICYT)-CMM, Fondecyt 1180706 (G.A.R.) for financially supporting this research.
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Acknowledgements
The authors would like to thank CONICYT-Chile under grant Fondecyt 11160072 (M.A.V.) and Basal (CONICYT)-CMM, Fondecyt 1180706 (G.A.R.) for financially supporting this research. We thank Professor Sergio Rica for his early participation and help provided in this work.
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Valle, M.A., Ruz, G.A. (2019). Market Basket Analysis Using Boltzmann Machines. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_49
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