Abstract
Association rules is the traditional way used to study market basket or transactional data. One drawback of this analysis is the huge number of rules generated. As a complement to association rules, Association Rules Network (ARN), based on Social Network Analysis (SNA) has been proposed by several researchers. In this work we study a real market basket analysis problem, available in a Belgian supermarket, using ARNs. We learn ARNs by considering the relationships between items that appear more often in the consequent of the association rules. Moreover, we propose a more compact variant of ARNs: the Maximal Itemsets Social Network. In order to assess the quality of these structures, we compute SNA based metrics, like weighted degree and utility of community.
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Rodrigues, M., Gama, J., Ferreira, C.A. (2012). Identifying Relationships in Transactional Data. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds) Advances in Artificial Intelligence – IBERAMIA 2012. IBERAMIA 2012. Lecture Notes in Computer Science(), vol 7637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34654-5_9
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DOI: https://doi.org/10.1007/978-3-642-34654-5_9
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