关键词: 基于范例推理, 文本自动分类, K近邻, 聚类

Abstract: K-Nearest Neighbor(KNN) is one of the top-performing classifiers,but it has a large time complexity on calculating the similarity between the document and all training samples.An automatic text categorization mechanism based on CBR was presented,the training sample library was converted to the case library and the document was classified by KNN.In experiments,the average recall and precision were 87.07% and 89.17% respectively.In addition,by analyzing the time complexity,this mechanism can perform much more quickly than the KNN method.

Key words: case-based reasoning(CBR), automatic text categorization, K-nearest neighbor, clustering