【作者】 谢春丽；

【导师】 崔志明；

【作者基本信息】 苏州大学 ， 计算机应用技术， 2003， 硕士

【摘要】 本论文对基于数据挖掘的Web行为特征分析与研究系统做了阐述，系统旨在通过数据挖掘技术，从用户与Web服务器的交互数据中发现隐含的用户访问的规律，得到用户的访问模式和用户的兴趣，为用户的个性化服务提供基础。本文以Web服务器日志、Web拓扑结构作为数据源，从数据收集、数据预处理和数据分析三个阶段来阐述系统的整体结构和实现，挖掘用户的频繁访问浏览页和网页间的关联。并结合网页特点，考虑到主页的点击率的影响，对生成频繁访问浏览页的算法做了改进；在Web使用挖掘的基础上引入部分Web结构挖掘，对挖掘浏览页的关联规则做了补充，在Web结构挖掘基础上挖掘出的相关浏览页也推荐给用户，在一定程度上提高了关联规则的精确度。更多还原

【Abstract】 This paper describes Web behavior character analysis and research system, which uncovers the hidden regulations among the interactive data between a Web server and its users. According to data mining, this paper aims to find user access patterns and interesting. The data resource of this system is Web logs and Web site topology. This paper divides the web usage mining process into three main parts: data collecting consists of the three server logs - access, referer, and agent, the HTML files the make up the site, and registration data; data preprocessing ,includes data cleaning, user identification, session identification, path completion and transaction identification; data analysis consists of mining frequent access paths and association rule . Thinking about the amount of hits on homepage, this paper improves the algorithm of finding frequent items. Thinking about the character of Web site design, this paper presents a algorithm about association rules based on Web structure mining.更多还原