关键词: Canopy聚类, 粗糙集, [K]-最近邻（KNN）算法, 文本分类

Abstract: Focused on the problem that the classification efficiency of KNN algorithm is gradually reduced with the increase of training set size and feature dimension, the CRS-KNN text classification algorithm based on Canopy and rough set is proposed in this paper. Firstly, the text data to be processed is clustered by Canopy. For each obtained cluster, upper and lower approximate segmentation with rough set theory is taken. The lower approximate area obtained by dividing does not need classification, but the border area which is acquired by the difference of upper and lower approximate needs final classification by KNN algorithm. Experimental results show that the proposed algorithm reduces the size of the data computing about KNN algorithm, and improves the classification efficiency. At the same time, the accuracy rate, recall rate and [F1] value are improved compared with the traditional KNN algorithm and improved KNN text classification algorithm based on clustering.

Key words: Canopy clustering, rough set, [k]-Nearest Neighbor（KNN） algorithm, text classification