连续属性空间上的规则学习算法

A Rule Learning Algorithm on Continuous Attributes Space

文章研究连续属性空间上的规则学习算法．首先简述了研究连续属性空间上的规则学习算法的目的和意义，并将规则学习理论中的一些基本概念推广到连续属性空间．在此基础上，研究了连续属性空间离散化问题，证明了属性空间最小离散化问题是NP困难问题，并将信息熵函数与无穷范数的概念应用到连续属性离散化问题，提出了基于信息熵的属性空间极小化算法．最后，提出了连续属性空间上的规则学习算法，并给出了数值实验结果．

The rule learning algorithm on continuous attribute, space is studied in this paper. First, the purpose and the importance of studying rule learning algorithm on continuous attributes space are briefly introduced, and then some basic concepts in the theory of rule learning are extended to the continuous attributesspace. On this basis, the authors study the problem to divide continuous attributes space, and prove that theproblem of min dividing continuous attributes space is a NP hard problem. The concepts of information entropyand infinite normed apply to the problem of dividing continuous attribute space and a new algorithm of dividingcontinuous attribute space based on the function of information entropy are presented. At last, a rule learningalgorithm on continuous attributes space is presented and the data results of the experiments are given.