数值型数据的泛概念树的自动生成方法

概念层次在数据挖掘中有着重要的作用 .通过自动生成概念层次 ,可有效地提高数据挖掘的效率 ,在不同层次上发现知识 .文中介绍基于云模型的数值型概念表示方法 ,通过云模型的期望值、熵和超熵三个数字特征有效地表达定性概念 ,并实现定性和定量的不确定转换 .通过云变换实现了泛概念树中叶结点的自动生成 ,并自动构造数值型数据的泛概念树 .同时 ,进一步研究了泛概念树中的概念爬升和跳跃的方法 ,为通过数据挖掘发现各层次知识提供了基础 .

Automatic Generation of Pan-Concept-Tree on Numerical Data

Concept hierarchy plays a fundamentally important role in data mining. Through automatically generating the concept hierarchies, the mining efficacy is improved, and the knowledge is discovered at different abstraction levels. The method to represent numerical concept with cloud models is introduced. Qualitative concepts can be represented effectively with three digital parameters of cloud models: expected value Ex, entropy En and hype entropy He. Cloud transform is realized to automatically produce the basic numerical concepts as the leaf nodes in pan concept tree. Automatic generation of pan concept tree based on cloud models is also provided. Lastly, climbing up and jumping up on the pan concept tree is studied, as the basis of discovering all kinds of knowledge in different levels.