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A Hybrid Framework Using SOM and Fuzzy Theory for Textual Classification in Data Mining

  • Chapter
Modelling with Words

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2873))

Abstract

This paper presents a hybrid framework combining self-organising map (SOM) and fuzzy theory for textual classification. Clustering using self-organizing maps is applied to produce multiple targets. In this paper, we propose that an amalgamation of SOM and association rule theory may hold the key to a more generic solution, less reliant on initial supervision and redundant user interaction. The results of clustering stem words from text documents could be utilised to derive association rules which designate the applicability of documents to the user. A four stage process is consequently detailed, demonstrating a generic example of how a graphical derivation of associations may be derived from a repository of text documents, or even a set of synopses of many such repositories. This research demonstrates the feasibility of applying such processes for data mining and knowledge discovery.

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Author information

Authors and Affiliations

  1. Centre for Information Technology Innovation, Faculty of Information Technology, Queensland University of Technology, Brisbane, QLD, Australia

    Yi-Ping Phoebe Chen

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  1. Yi-Ping Phoebe Chen
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Editor information

Editors and Affiliations

  1. Artificial Intelligence Group, Engineering Mathematics Department, University of Bristol, BS8 1TR, UK

    Jonathan Lawry

  2. Clairvoyance Corporation, 5301 Fifth Avenue, 15232, Pittsburgh, PA, USA

    Jimi Shanahan

  3. ECECS Department, University of Cincinnati, ML 0030, 45221-0030, Cincinnati, OH, USA

    Anca L. Ralescu

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© 2003 Springer-Verlag Berlin Heidelberg

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Chen, YP.P. (2003). A Hybrid Framework Using SOM and Fuzzy Theory for Textual Classification in Data Mining. In: Lawry, J., Shanahan, J., L. Ralescu, A. (eds) Modelling with Words. Lecture Notes in Computer Science(), vol 2873. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39906-3_8

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  • DOI: https://doi.org/10.1007/978-3-540-39906-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20487-9

  • Online ISBN: 978-3-540-39906-3

  • eBook Packages: Springer Book Archive

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