Mining user access patterns with traversal constraint for predicting web page requests

Mei Ling Shyu, Choochart Haruechaiyasak, Shu Ching Chen

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

The recent increase in HyperText Transfer Protocol (HTTP) traffic on the World Wide Web (WWW) has generated an enormous amount of log records on Web server databases. Applying Web mining techniques on these server log records can discover potentially useful patterns and reveal user access behaviors on the Web site. In this paper, we propose a new approach for mining user access patterns for predicting Web page requests, which consists of two steps. First, the Minimum Reaching Distance (MRD) algorithm is applied to find the distances between the Web pages. Second, the association rule mining technique is applied to form a set of predictive rules, and the MRD information is used to prune the results from the association rule mining process. Experimental results from a real Web data set show that our approach improved the performance over the existing Markov-model approach in precision, recall, and the reduction of user browsing time.

Original languageEnglish (US)
Pages (from-to)515-528
Number of pages14
JournalKnowledge and Information Systems
Volume10
Issue number4
DOIs
StatePublished - Nov 1 2006

Keywords

  • Associationrule mining
  • Mininguser access patterns
  • Web usage mining

ASJC Scopus subject areas

  • Information Systems

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