基于K-means的政府公文聚类方法  

Methods of Government Document Clustering Based on K-means Algorithm

作　　者：王荻智 李建宏 施运梅[1] WANG Di-zhi;LI Jian-hong;SHI Yun-mei(Computer School,Beijing Information Science and Technology University,Beijing 100101,China)

机构地区：[1]北京信息科技大学计算机学院,北京100101

出　　处：《软件导刊》2020年第6期201-204,共4页Software Guide

基　　金：国家重点研发计划项目(2018YFB1004100);北京信息科技大学2019年人才培养质量提高经费项目(5101923400)。

摘　　要：政府公文数量巨大,不同政府网站公文分类规则不一,在引用和参考公文时可能发生混淆。针对该问题,基于政府公文题目、摘要和正文内容,采用K-means算法对公文进行分类。首先对政府公文进行分词及去停用词等数据预处理操作,再通过词频-逆文档频率(TF-IDF)权值计算方法,将处理后的政府文本信息转换成二维矩阵,然后采用K-means算法进行聚类。使用清华大学THUCTC文本分类系统对公文聚类结果进行测试。实验结果表明,采用K-means算法对公文进行聚类,准确率达到82.93%,远高于政府网站公文分类准确率。The number of government documents is huge,and each government body uses different methods to classify their documents,but the results are not satisfactory. The article proposes a method which uses K-means algorithm to classify the government documents based on their titles,abstracts and contents. Firstly,the government documents are preprocessed by word segmentation,removing stop words and etc. Then TF-IDF weight calculation algorithm is used to quantify the government documents. Finally,K-means is utilized to cluster the government documents. THUCTC developed by Tsinghua University is used to test the results of the document classification and the accuracy rate reaches 82.93%,the experiment shows that K-means can achieve higher clustering effects.

关 键 词：文本聚类 词频—逆文档频率 K-MEANS算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]