Using Random walks for mining web document associations

Kasim Candan, Wen Syan Li

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

15 Scopus citations


World Wide Web has emerged as a primetry means for storing and structuring information. In this paper, we present a framework for mining implicit associations among Web documents. We focus on the following problem: “For a given set of seed URLs, find a list of Web pages which reflect the association among these seeds.” In the proposed framework, associations of two documents are induced by the connectivity and linking path length. Based on this framework, we have developed a random walk-hased Web mining technique and validated it by experiments on real Web data. In this paper, we also discuss the extension of the algorithm for considering document contents.

Original languageEnglish (US)
Title of host publicationKnowledge Discovery and Data Mining
Subtitle of host publicationCurrent Issues and New Applications - 4th Pacific-Asia Conference, PAKDD 2000, Proceedings
EditorsArbee L.P. Chen, Takao Terano, Huan Liu
PublisherSpringer Verlag
Number of pages12
ISBN (Print)3540673822, 9783540673828
StatePublished - Jan 1 2000
Event4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 - Kyoto, Japan
Duration: Apr 18 2000Apr 20 2000

Publication series

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


Other4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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