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STEPWISE ASSOCIATION RULE EXTRACTION METHOD BASED ON BOOLEAN EXPRESSION FOR DYNAMIC DATA

机译:基于布尔表达式的动态数据逐步关联规则提取方法

摘要

The present invention relates to an algorithm for expressing and describing preference or history of a user modeled as an aggregate form based on a boolean expression form with a higher expressiveness than a set, and extracting a conclusion set corresponding to the same. Particularly, a dynamic data set that is frequently updated or has a short update period is a target to extract association rule, and a stepwise association rule extraction methodology is proposed to quickly provide a conclusion set for frequently changing large data. Therefore, the present invention expands a type of a premise Q expressing preference or history of a user from the existing set to boolean expressions so as to more clearly reflect state and situation of the user and to provide a conclusion R corresponding to the premise Q expressed in detail, which means extension and improvement of the existing association rule extraction methodology. Also, unlike existing algorithms for static data, the present invention is for dynamically changing data. Therefore, it is possible to have a high commercial competitiveness not only in traditional association rule utilization fields but also in fields that process data changing in real-time such as SNS.;COPYRIGHT KIPO 2017
机译:本发明涉及一种算法,该算法用于基于具有比集合更高的表达性的布尔表达形式来表达和描述被建模为聚合形式的用户的偏好或历史,并提取与之相对应的结论集。特别地,频繁更新或更新周期短的动态数据集是提取关联规则的目标,并且提出了逐步关联规则提取方法以快速提供用于频繁变化的大数据的结论集。因此,本发明将表示用户的喜好或历史的前提Q的类型从现有集合扩展为布尔表达式,以便更清楚地反映用户的状态和状况,并提供与所表示的前提Q相对应的结论R详细地讲,这意味着扩展和改进现有的关联规则提取方法。而且,与现有的用于静态数据的算法不同,本发明用于动态改变数据。因此,不仅在传统的关联规则使用领域中,而且在SNS等实时处理数据变化的领域中都可能具有很高的商业竞争力。; COPYRIGHT KIPO 2017

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