基于属性量-质特性转化及其定性映射的KDD模型
A KDD MODEL BASED ON CONVERSION OF QUANTITY QUALITY FEATURES OF ATTRIBUTES
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摘要: 指出了若将规则看作是一种广义的特征 ,则从数据中挖掘规则 (或知识 ) ,可用属性量 -质特征转化的定性映射加以刻画 .根据特征定性可随定性基准而变的事实 ,并给出了一个高效、可增长、可重泛化的概念树生成算法 ,即EIGR算法 ,与传统的 KDD方法相比 ,该方法不仅更符合于人类思维 ,而且 ,所发现的知识更具强壮性和稳健性Abstract: Mining the association rules (or knowledge) from data can be described by a qualitative mapping based on conversion of quantity quality features of attributes. An efficient incremental generalization and regeneralization algorithm is given in this paper.
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