计算机工程与应用 ›› 2007, Vol. 43 ›› Issue (31): 123-125.

• 网络、通信与安全 • 上一篇    下一篇

挖掘Web日志中的分类关联规则

林文龙,刘业政,姜元春,焦 宁   

  1. 合肥工业大学 电子商务研究所,合肥 230009
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-11-01 发布日期:2007-11-01
  • 通讯作者: 林文龙

Mining class association rules in Web log data

LIN Wen-long,LIU Ye-zheng,JIANG Yuan-chun,JIAO Ning   

  1. Institute of E-Business,Hefei University of Technology,Hefei 230009,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-11-01 Published:2007-11-01
  • Contact: LIN Wen-long

摘要: 用户分类是Web访问模式挖掘研究的一个重要任务。提出一种应用关联分类技术对Web用户进行分类的方法:首先通过对Web日志文件预处理得到训练事务数据集,然后从该事务集中挖掘分类关联规则,并利用所挖掘的规则集构建了一个分类器,从而实现了根据用户访问历史对用户进行分类。

Abstract: Web users classification is an important issue in Web access pattern mining research area.A method of classifying Web users using associative classification technique is proposed.First the train transaction dataset is obtained by pre-processing Web log file,then the class association rules is discovered from the dataset,finally a classifier based on the discovered rules set is built.By doing so,we realize users classification according to their access history.