Identifying interesting visitors through web log classification

Yu, JX; Ou, Y; Zhang, C; Zhang, S

Identifying interesting visitors through web log classification

Yu, JX

Ou, Y Zhang, C

Zhang, S

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Publication Type:: Journal Article
Citation:: IEEE Intelligent Systems, 2005, 20 (3), pp. 55 - 60
Issue Date:: 2005-05-01

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Field	Value	Language
dc.contributor.author	Yu, JX https://orcid.org/0000-0002-9738-827X	en_US
dc.contributor.author	Ou, Y	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2005-05-01	en_US
dc.identifier.citation	IEEE Intelligent Systems, 2005, 20 (3), pp. 55 - 60	en_US
dc.identifier.issn	1541-1672	en_US
dc.identifier.uri	http://hdl.handle.net/10453/5778
dc.description.abstract	The use of web log classification to identify the customer with a small data set is discussed. Web mining is a popular technique for analyzing visitor activities in e-service systems, which include, web text mining, web structure mining, and web log mining. Several groups of experiments on Dell Workstation PWS650 with 2 Gbytes of main memory running Window 2000 are conducted to evaluate the web log mining technique. The results show that when one classifies the 39,033 log records using the three classifiers, removing one attribute at a time, confirms that it's hard to determine which attribute to remove in order to achieve high accuracy.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation.ispartof	IEEE Intelligent Systems	en_US
dc.relation.isbasedon	10.1109/MIS.2005.47	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Identifying interesting visitors through web log classification	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	20	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

The use of web log classification to identify the customer with a small data set is discussed. Web mining is a popular technique for analyzing visitor activities in e-service systems, which include, web text mining, web structure mining, and web log mining. Several groups of experiments on Dell Workstation PWS650 with 2 Gbytes of main memory running Window 2000 are conducted to evaluate the web log mining technique. The results show that when one classifies the 39,033 log records using the three classifiers, removing one attribute at a time, confirms that it's hard to determine which attribute to remove in order to achieve high accuracy.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/5778