Continuous attribute discretization algorithm of Rough Set based on k-means

In the application of the Rough Set theory to preprocess the data, continuous attribute discretization is the necessary and key step. Here, a discretization method based on the k-means algorithm was introduced. Using this method, the wholly attributes could be classified into 2 categories. Four sets of data on UCI database were chosen to verify the performance of the presented method. In this experiment, the k-means algorithm was used to implement the data discretization firstly; and then they are used to do attributes reduction through rough set; finally, the classification result is validated with KNN (k-Nearest Neighbor algorithm, k=10) classifier classification algorithm. The experimental results show that this method presented in this paper can improve the efficiency of discretization, and effectively reduce the break points.