Abstract
Most of the real market basket data are non-binary in the sense that an item could be purchased multiple times in the same transaction. In this case, there are two types of occurrences of an itemset in a database: the number of transactions in the database containing the itemset, and the number of occurrences of the itemset in the database. Traditional support-confidence framework might not be adequate for extracting association rules in such a database. In this paper, we introduce three categories of association rules. We introduce a framework based on traditional support-confidence framework for mining each category of association rules. We present experimental results based on two databases.
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Adhikari, A., Rao, P.R. (2008). Association Rules Induced by Item and Quantity Purchased. In: Haritsa, J.R., Kotagiri, R., Pudi, V. (eds) Database Systems for Advanced Applications. DASFAA 2008. Lecture Notes in Computer Science, vol 4947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78568-2_37
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DOI: https://doi.org/10.1007/978-3-540-78568-2_37
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