Abstract: Attribute reduction in rough set theory is a NP-hard problem，which is studied mainly to design a more efficient algorithm.Aiming at the problem of inefficiency and low velocity with the traditional attribute reduction algorithm，an attribute reduction algorithm based on correlation coefficient and conditional information entropy is proposed，which changes attribute reduction process of non core attributes in the decision table into calculation of correlation coefficient，reduces the number of scanning decision table，algorithmic time complexity and redundancy of the algorithm，and improves the efficiency of attribute reduction.Then the k-fold rotation comparison method is used to calculate correlation coefficient，which largely reduces calculation amount，and attains sub optimal attribute reduction result.The algorithm details are given，and an experiment is carried out，the result of which verifies the efficiency of the algorithm.

Key words: rough set, attribute reduction, correlation coefficient

摘要： 粗糙集的属性约简是一个NP难问题，获得较为高效的算法是研究的主要目的。针对传统的粗糙集属性约简算法效率不高、速度不快的问题，提出基于相关系数和条件信息熵的属性约简算法，把决策表的非核属性约简过程转化为相关系数的运算，能减少对决策表的扫描次数，降低算法时间复杂度，降低算法冗余，提高属性约简的效率。并利用k-fold轮换对比方法计算相关系数，较大地减少了计算量，同时能得到次优属性约简结果。给出了算法内容，并结合实验进行了验证。

关键词: 粗糙集, 属性约简, 相关系数