Key words: Data mining, Web log, Users browse path, Users browse time, Clustering algorithm
目 录
中文摘要········································································································ I 英文摘要······································································································· II 1 绪论 1
研究的背景和意义························································································1
研究现状····································································································2
国外研究现状·························································································3
国内研究现状·························································································3
本文主要工作······························································································5
2 Web 挖掘理论与技术 6
Web 挖掘 6
Web 日志挖掘 7
Web 日志挖掘数据来源 7
Web 日志格式 8
Web 日志挖掘预处理 8
本章小结·································································································· 11
3 基于用户兴趣模糊聚类算法研究······················································· 12
聚类的基本概述························································································ 12
数据表示·································································································· 14
相似性度量······························································································· 14
相似性的定义······················································································ 14
距离计算的几种方法············································································· 14
相似度系数的计算方法·········································································· 15
基于浏览时间和用户路径的相似度算法·························································· 16
Web 用户信息的采集 16
计算用户的浏览时间············································································· 16
构建时间相关矩阵····················
基于用户行为的web日志聚类分析与应用 来自淘豆网m.daumloan.com转载请标明出处.
