Pattern mining for a two-stage information filtering system

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Scopus Citations
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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication15th Pacific-Asia Conference, PAKDD 2011, Proceedings
PublisherSpringer Verlag
Pages363-374
Volume6634 LNAI
ISBN (Print)9783642208409
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6634 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011
PlaceChina
CityShenzhen
Period24 - 27 May 2011

Abstract

As information available over computer networks is growing exponentially, searching for useful information becomes increasingly more difficult. Accordingly, developing an effective information filtering mechanism is becoming very important to alleviate the problem of information overload. Information filtering systems often employ user profiles to represent users' information needs so as to determine the relevance of documents from an incoming data stream. 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 two-stage filtering model significantly outperforms both the term-based and pattern-based information filtering models. © 2011 Springer-Verlag.

Research Area(s)

  • Information filtering, Pattern mining, Threshold, User profile

Citation Format(s)

Pattern mining for a two-stage information filtering system. / Zhou, Xujuan; Li, Yuefeng; Bruza, Peter et al.
Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Proceedings. Vol. 6634 LNAI Springer Verlag, 2011. p. 363-374 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6634 LNAI).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review