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A Text Mining Agents Based Architecture for Personal E-mail Filtering and Management

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Intelligent Data Engineering and Automated Learning — IDEAL 2002 (IDEAL 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2412))

Abstract

E-mail messages can be modeled as semi-structured documents that consist of a set of classes and a number of variable length free-text. Thus, many text mining techniques can be used to develop a personal e-mail filtering and management system. This paper addresses a text mining agents based architecture, in which two kinds of text mining agents: USPC (uncertainty sampling based probabilistic classifier) and R2L (rough relation learning) are used cooperatively, for personal e-mail filtering and management.

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Author information

Authors and Affiliations

  1. Dept. of Information Eng., Maebashi Institute of Technology, 460-1 Kamisadori-Cho, Maebashi-City, 371-0816, Japan

    Ning Zhong & Takahisa Matsunaga

  2. School of Computer Science, Beijing Polytechnic University, Beijing, 100022, P.R. China

    Chunnian Liu

Authors
  1. Ning Zhong
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  2. Takahisa Matsunaga
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  3. Chunnian Liu
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Electronics, UMIST, Manchester, M60 1QD, UK

    Hujun Yin , Nigel Allinson  & Richard Freeman ,  & 

  2. Department of Computation, UMIST, Manchester, M60 1QD, UK

    John Keane

  3. Department of Biomolecular Science, UMIST, Manchester, M60 1QD, UK

    Simon Hubbard

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© 2002 Springer-Verlag Berlin Heidelberg

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Zhong, N., Matsunaga, T., Liu, C. (2002). A Text Mining Agents Based Architecture for Personal E-mail Filtering and Management. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45675-9_50

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  • DOI: https://doi.org/10.1007/3-540-45675-9_50

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44025-3

  • Online ISBN: 978-3-540-45675-9

  • eBook Packages: Springer Book Archive

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