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A Study on the Modified Attribute Oriented Induction Algorithm of Mining the Multi-value Attribute Data

机译:改进的面向属性的多值属性数据挖掘归纳算法研究

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Attribute Oriented Induction method (short for AOI) is one of the most important methods of data mining. The input value of AOI contains a relational data table and attribute-related concept hierarchies. The output is a general feature inducted by the related data. Though it is useful in searching for general feature with traditional AOI method, it only can mine the feature from the single-value attribute data. If the data is of multiple-value attribute, the traditional AOI method is not able to find general knowledge from the data. In addition, the AOI algorithm is based on the way of induction to establish the concept hierarchies. Different principles of classification or different category values produce different concept trees, therefore, affecting the inductive conclusion. Based on the issue, this paper proposes a modified AOI algorithm combined with a simplified Boolean bit Karnaugh map. It does not need to establish the concept tree. It can handle data of multi value and find out the general features implied within the attributes.
机译:面向属性的归纳方法(AOI的缩写)是最重要的数据挖掘方法之一。 AOI的输入值包含一个关系数据表和与属性相关的概念层次结构。输出是由相关数据引起的一般特征。尽管它对使用传统AOI方法搜索一般特征很有用,但它只能从单值属性数据中挖掘特征。如果数据具有多值属性,则传统的AOI方法无法从数据中找到常识。另外,AOI算法基于归纳的方式来建立概念层次。不同的分类原则或不同的类别值会产生不同的概念树,因此影响归纳结论。基于这个问题,本文提出了一种结合简化的布尔比特卡诺图的改进的AOI算法。它不需要建立概念树。它可以处理多值数据,并找出属性中隐含的一般特征。

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