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- W342119470 abstract "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.KeywordsAssociation RulesAssociation Rule MiningItemsetsBinary DatabaseSupp 2dThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W342119470 created "2016-06-24" @default.
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- W342119470 date "2014-12-28" @default.
- W342119470 modified "2023-09-25" @default.
- W342119470 title "Mining Association Rules Induced by Item and Quantity Purchased" @default.
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- W342119470 doi "https://doi.org/10.1007/978-3-319-13212-9_5" @default.
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