Information entropy discretization algorithm based on ranking means clustering
基于逐级均值聚类的信息熵的离散化算法

The recent discrete algorithms are very difficult to achieve high efficiency and high recognition rate of both. This paper proposed an information entropy discretization algorithm based on ranking means clustering. Firstly, used ranking means clustering method for analyzing information entropy value of each candidate cuts, and generated a new candidate cuts set. Se-condly, used information entropy method for completing the selection of cuts for the discretization of continuous attributes va-lues. Finally, simulation experiment results show that the method has lower time complexity than traditional methods.