基于信息熵的粗糙集连续属性离散化算法

该文提出了一种新的粗糙集连续属性离散化算法．首先对每一个候选断点定义了信息熵，以此作为对断点重要性的量度，在此基础上给出了断点选择的粗糙集连续属性离散化算法．最后采用多组数据对此算法的性能进行了检验，并与其它算法做了对比实验．实验结果表明此算法是有效的，而且当候选断点个数增多时仍有很高的计算效率．

Discretization of Continuous Attributes in Rough Set Theory Based on Information Entropy

In this paper a new discretization algorithm of continue attributes in rough set is offered. Firstly, a information entropy is defined for every candidate cut point and treated as a measurement of importance. On the basis of that, a discretization algorithm of continue attributes in rough set for selecting cut points is illustrated. Finally, a group of data set is applied to test the performance of the algorithm and the experiment result is compared with other discretization algorithm. The experiment result shows that the algorithm is effective, and keeps a high computing efficiency when the number of candidate cut point increase.