Paper Info
Title
Web usage mining using support vector machine
Abstract
The web contains rich and dynamic collections of hyperlink information, web page access, and usage information providing rich sources for data mining. From this, we need a system to recommend a visitor good information. This recommendation system can be constructed by web usage mining process. The web usage mining mines web log records to discover user access patterns of web pages. Also it is the application of data mining techniques to large web log data in order to extract usage patterns from user's click streams. In general, the size of web log records is so large that we have difficulty to analyze web log data. To make matter worse, the web log records are very sparse. So it is very hard to estimate the dependency between the web pages. In this paper, we solved this difficulty of web usage mining using support vector machine. In the experiments, we verified our proposed method by given data from UCI machine learning repository and KDD cup 2000.
Year
DOI
Venue
2005
10.1007/11494669_43
IWANN
Keywords
Field
DocType
web usage mining mine,support vector machine,data mining,web log record,web page,large web log data,web usage mining process,web log data,web usage mining,web page access,data mining technique,web pages,recommender system,machine learning
Static web page,Web mining,Web page,Web analytics,Computer science,Data Web,Web log analysis software,Web modeling,Web navigation,Database
Conference
Volume
ISSN
ISBN
3512
0302-9743
3-540-26208-3
Citations 
PageRank 
References 
3
0.40
6
Authors
1
Name
Order
Citations
PageRank
Sung-Hae Jun19511.79