Combining the web content and usage mining to understand the visitor behavior in a web site

Juan Velásquez, Hiroshi Yasuda, Terumasa Aoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

A web site is a semi structured collection of different kinds of data, whose motivation is show relevant information to visitor and by this way capture her/his attention. Understand the specifics preferences that define the visitor behavior in a web site, is a complex task. An approximation is suppose that it depend the content, navigation sequence and time spent in each page visited. These variables can be extracted from the web log files and the web site itself, using web usage and content mining respectively. Combining the describe variables, a similarity measure among visitor sessions is introduced and used in a clustering algorithm, which identifies groups of similar sessions, allowing the analysis of visitors behavior. In order to prove the methodology's effectiveness, it was applied in a certain web site, showing the benefits of the described approach.

Original languageEnglish
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Pages669-672
Number of pages4
Publication statusPublished - 2003 Dec 1
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: 2003 Nov 192003 Nov 22

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other3rd IEEE International Conference on Data Mining, ICDM '03
CountryUnited States
CityMelbourne, FL
Period03/11/1903/11/22

ASJC Scopus subject areas

  • Engineering(all)

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