Research on mining user browsing patterns in large web logs based on Poisson sampling and Sequence Alignment Method

被引：0

作者：

Liu, Peiqian ^{[1
]}

An, Jiyu ^{[1
]}

Guo, Hairu ^{[1
]}

机构：

[1] Henan Polytech Univ Jiaozuo, Sch Comp Sci & Technol, Kaifeng 454003, Peoples R China

来源：

2008 PROCEEDINGS OF INFORMATION TECHNOLOGY AND ENVIRONMENTAL SYSTEM SCIENCES: ITESS 2008, VOL 4 | 2008年

关键词：

data mining; Poisson sampling; Sequence Alignment Method (SAM);

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The continuous growth in the size and use of the Internet is creating large sever logs on web servers and difficulties in the search for information. A sophisticated method to organize the layout of the information and assist user navigation is therefore particularly important. In this paper, we valuate the feasibility of using a Poisson sampling and SAM to mine large web log data. A sample sets selected by Poisson sampling statistically effectively represent the characteristics of the entire dataset. In addition, users are partitioned into clusters using a non-Euclidean distance measure, called Sequence Alignment Method (SAM).

引用

页码：1119 / 1121

页数：3