基于模糊近似度的Web文本过滤模型  

The Feature Acquiring Algorithm on The Web Text

作　　者：刘明吉[1] 饶一梅[1] 王秀峰[1] 黄亚楼[1] 

机构地区：[1]南开大学信息技术科学学院,天津300071

出　　处：《计算机科学》2001年第12期55-58,共4页Computer Science

基　　金：天津自然科学基金(003700111)和(993600811)

摘　　要：The booming growth of the Internet provides us a great deal of information resource. In this paper, we create a text filtering model based on VSM. In this model,Web text mming is an efficient technique,which discoveres valuable and potential knowledge from those unstructured texts. In this paper,we use VSM as the description of Web text and give a feature subset algorithm which is based on the Genetic Algorthm. This algorithm can greatly improve the efficiency of dealing with Web texts and give much better way to classify and cluster the texts. Our experiments show that this method is active well in feature dimension reduction.The booming growth of the Internet provides us a great deal of information resource. In this paper, we create a text filtering model based on VSM. In this model, Web text mining is an efficient technique,which discoveres valuable and potential knowledge from those unstructured texts. In this paper, we use VSM as the description of Web text and give a feature subset algorithm which is based on the Genetic Algorthm. This algorithm can greatly improve the efficiency of dealing with Web texts and give much better way to classify and cluster the texts. Our experiments show that this method is active well in feature dimension reduction.

关 键 词：WWW WEB 文本过滤模型 模糊近似度 INTERNET 数据库 

分 类 号：TP393.4[自动化与计算机技术—计算机应用技术]