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 chapter, 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., Adhikari, J. (2015). Mining Association Rules Induced by Item and Quantity Purchased. In: Advances in Knowledge Discovery in Databases. Intelligent Systems Reference Library, vol 79. Springer, Cham. https://doi.org/10.1007/978-3-319-13212-9_5
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DOI: https://doi.org/10.1007/978-3-319-13212-9_5
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