A clustering model for mining evolving web user patterns in data stream environment

Edmond H. Wu*, Michael K. Ng, Andy M. Yip, Tony F. Chan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

Abstract

With the fast growing of the Internet and its Web users all over the world, how to manage and discover useful patterns from tremendous and evolving Web information sources become new challenges to our data engineering researchers. Also, there is a great demand on designing scalable and flexible data mining algorithms for various time-critical and data-intensive Web applications. In this paper, we purpose a new clustering model for generating and maintaining clusters efficiently which represent the changing Web user patterns in Websites. With effective pruning process, the clusters can be fast discovered and updated to reflect the current or changing user patterns to Website administrators. This model can also be employed in different Web applications such as personalization and recommendation systems.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsZheng Rong Yang, Richard Everson, Hujun Yin
PublisherSpringer Verlag
Pages565-571
Number of pages7
ISBN (Print)3540228810, 9783540228813
DOIs
StatePublished - Jan 1 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3177
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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