A generalization-based approach to clustering of Web usage sessions

Yongjian Fu, Kanwalpreet Sandhu, Ming-Yi Shih

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

72 Citations (Scopus)

Abstract

The clustering of Web usage sessions based on the access patterns is studied. Access patterns of Web users are extracted from Web server log files, and then organized into sessions which represent episodes of interaction between the Web users and the Web server. Using attribute-oriented induction, the sessions are then generalized according to a page hierarchy which organizes pages based on their contents. These generalized sessions are finally clustered using a hierarchical clustering method. Our experiments on a large real data set show that the approach is efficient and practical for Web mining applications.

Original languageEnglish
Title of host publicationWeb Usage Analysis and User Profiling - International WEBKDD 1999 Workshop, Revised Papers
EditorsBrij Masand, Brij Masand, Myra Spiliopoulou
PublisherSpringer Verlag
Pages21-38
Number of pages18
ISBN (Electronic)9783540678182
Publication statusPublished - 2000 Jan 1
EventInternational Workshop on Web Usage Analysis and User Profiling, WEBKDD 1999 - San Diego, United States
Duration: 1999 Aug 151999 Aug 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1836
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshop on Web Usage Analysis and User Profiling, WEBKDD 1999
CountryUnited States
CitySan Diego
Period99-08-1599-08-15

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Fu, Y., Sandhu, K., & Shih, M-Y. (2000). A generalization-based approach to clustering of Web usage sessions. In B. Masand, B. Masand, & M. Spiliopoulou (Eds.), Web Usage Analysis and User Profiling - International WEBKDD 1999 Workshop, Revised Papers (pp. 21-38). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1836). Springer Verlag.