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System and method for mining generalized association rules in databases

机译:在数据库中挖掘广义关联规则的系统和方法

摘要

A system and method for discovering consumer purchasing tendencies includes a computer-implemented program which identifies consumer transaction itemsets that are stored in a database and which appear in the database a user-defined minimum number of times, referred to as minimum support. The itemsets contain items that are characterized by a hierarchical taxonomy. Then, the system discovers association rules, potentially across different levels of the taxonomy, in the itemsets by comparing the number of times each of the large itemsets appears in the database to the number of times particular subsets of the itemset appear in the database. When the relationship exceeds a predetermined minimum confidence value, the system outputs a generalized association rule which is representative of purchasing tendencies of consumers. The set of generalized association rules can be pruned of uninteresting rules, i.e., association rules which do not occur at a frequency that is significantly different than what is expected based upon the frequency of occurrence of the rule's ancestors.
机译:用于发现消费者购买倾向的系统和方法包括计算机执行的程序,该程序识别存储在数据库中并且在数据库中出现用户定义的最小次数(称为最小支持)的消费者交易项目集。项集包含以分层分类法为特征的项。然后,系统通过将每个大型项目集出现在数据库中的次数与该项目集的特定子集出现在数据库中的次数进行比较,来发现项目集中潜在的跨越分类法不同级别的关联规则。当关系超过预定的最小置信度值时,系统输出代表消费者购买倾向的通用关联规则。可以将无意义的规则(即,不会以与基于规则祖先的出现频率所期望的频率显着不同的频率出现)的关联规则修剪掉广义关联规则集。

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