Abstract：In order to discrete the continuous attribute before application of rough set theory, this paper utilized a discretization method based on the k-means algorithm, which discreted attributes of unsupervised clustering method into two categories. Four sets of data on UCI database were chosen for experiment to verify the performance of the proposed method. First step was to discrete the data, and then they were used to do attributes reduction through rough set and recognition by kNN (k=10) classifier classification algorithm. The results then was compared with other two discretization methods. The experimental results show that this method can improve the efficiency of discretization, reduce the complexity of the experiment, and effectively reduce the break points.
陈贞，邢笑雪. 粗糙集连续属性离散化的k均值方法[J]. 辽宁工程技术大学学报(自然科学版), 2015, 34(5): 642-646.
CHEN Zhen, XING Xiaoxue. The k-means method of discretization for continuous attribute in rough set theory. Journal of LNTU.Natural Science, 2015, 34(5): 642-646.