摘要
提出一种Web日志挖掘算法,该算法首先以Web站点的URL为行、以用户的UserID为列,建立URL- UserID关联矩阵,元素值为用户的访问次数;然后,对行向量进行相似性度量获得用户会话粗聚类,最后,利用层次结构对比聚类算法,对用户会话粗聚类进行进一步地处理得到更高精度的聚类,实验表明该算法在提高聚类精度方面卓有成效。
Similar customer groups, relevant Web pages and frequent access paths can be discovered by analyzing Web log files. A Web log mining algorithm is presented here. Firstly, according to Web site' s directed graph defined, a URL-UserID relevant matrix is set up, with URL as row and UserID as column, and users times of visiting as element values. Secondly, rough session clusters are obtained by measuring similarity between row vectors. Finally, by dealing with the rough session clusters further through hierarchy comparison clustering algorithm, clusters with higher precision can be acquired. Experiments prove the effectiveness of the algorithm.
出处
《河南科技大学学报（自然科学版）》
CAS
2006年第2期49-51,共3页
Journal of Henan University of Science And Technology:Natural Science
基金
河南省自然科学基金项目(0411010500)
关键词
网络
WEB日志挖掘
会话聚类
结构层次
Networks
Web log mining
Session clustering
Structure hierarchy
