关键词提取的K-means方法在设备分类中的运用  

Keyword Extraction Method Based on K-means in the Application of Equipment Classification

作　　者：陈立[1,2] 田端正[1] 

机构地区：[1]浙江工商大学实验室与设备管理处,杭州310018 [2]浙江大学计算机与科学学院,杭州310058

出　　处：《计算机系统应用》2015年第12期277-282,共6页Computer Systems & Applications

基　　金：浙江省社会科学界联合会研究课题成果(2014Z052);浙江省教育厅科研项目(Y201432308)

摘　　要：利用文本分类技术对设备进行分类目前遇到的最大困难是,信息处理量的急剧增加造成分类过程中设备特征项维数的大幅增加,使得对设备的分类变得愈加困难,且效率愈来愈低.而关键词提取是提高文本分类效率的常用方法.根据设备文本描述的特点,以预先假定的初始关键词及其特征项词频来构建向量空间模型(VSM),在此基础上利用K-means算法将文本中的关键词提取出来.实验表明,基于K-means的关键词提取不仅大幅度地提高了设备分类效率,且分类准确性也得到了提高.Expository is the most common form in text classification. With the continuous increase of information, how to improve the efficiency of expository text classification is a hotspot in researching. Keyword extraction is used to improve the efficiency in text classification. This paper suggests to extraction keywords in the text based on the use of K-means algorithm by constructing the Vector Space Model （VSM） subject to pre-assumed initial keyword frequency. The KNN text classification algorithm verifies keywords extracting based on the use of K-means algorithm not only makes the classification accuracy slightly increased, but also reduce the dimension, thus realizing the text classification efficiency.

关 键 词：设备分类 K-MEANS 关键词 提取 降维 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]