Mining user access behavior on the WWW

Mei Ling Shyu, Shu Ching Chen, Choochart Haruechaiyasak

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


In this paper, an affinity-based approach that provides good similarity measures for Web document clustering to discover user access behavior on the World Wide Web (WWW) is proposed. The proposed approach generates the similarity measures for groups of Web documents by considering the user access patterns. Any clustering algorithm using better similarity measures should yield better clusters for discovering user access behavior. By utilizing the discovered user access behavior, for example, the companies can previsely target their potential customers and convince them to purchase their products or services in electronic commerce. An experiment on a real data set is conducted and the experimental result shows that the proposed approach yields a better performance than the Cosine coefficient and the Euclidean distance method under the partitioning around medoid (PAM) method.

Original languageEnglish (US)
Pages (from-to)1717-1722
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
StatePublished - Jan 1 2001


  • Affinity-based
  • Probabilistic model
  • User access behavior
  • Web document clustering

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture


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