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Association Rules Induced by Item and Quantity Purchased

机译:购买物品和数量引起的关联规则

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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.
机译:大多数实际的购物篮数据都是非二进制的,即可以在同一笔交易中多次购买商品。在这种情况下,数据库中项目集的出现次数有两种:包含该项目集的数据库中的事务数量和数据库中该项目集的出现次数。传统的支持信心框架可能不足以提取此类数据库中的关联规则。在本文中,我们介绍了三类关联规则。我们引入了一个基于传统支持信心框架的框架,用于挖掘每种关联规则。我们提出基于两个数据库的实验结果。

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