A Two-stage information filtering based on rough decision rule and pattern mining

Xujuan Zhou, Yuefeng Li, Peter Bruza, Yue Xu, Raymond Lau

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.
Original languageEnglish
Pages (from-to)326-332
Number of pages7
JournalJournal of Emerging Technologies in Web Intelligence
Issue number4
Publication statusPublished - 2010
Externally publishedYes


  • information filtering
  • pattern mining
  • rough set theory
  • user profiles


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