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A Generic and Extendible Multi-Agent Data Mining Framework

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Hybrid Artificial Intelligence Systems (HAIS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5572))

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Abstract

A generic and extendible Multi-Agent Data Mining (MADM) framework, EMADS (the Extendible Multi-Agent Data mining System) is described. The central feature of the framework is that it avoids the use of agreed meta-language formats by supporting a system of wrappers. The advantage offered is that the system is easily extendible, so that further data agents and mining agents can simply be added to the system. A demonstration EMADS framework is currently available. The paper includes details of the EMADS architecture and the wrapper principle incorporated into it. A full description and evaluation of the framework’s operation is provided by considering two MADM scenarios.

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Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Liverpool, Ashton Building, Ashton Street, Liverpool, L69 3BX, United Kingdom

    Kamal Ali Albashiri & Frans Coenen

Authors
  1. Kamal Ali Albashiri
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  2. Frans Coenen
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Editor information

Editors and Affiliations

  1. Grupo de Investigación GICAP, Área de Lenguajes Higher Polytechnic School, Universidad de Burgos, Burgos, Spain

    Emilio Corchado

  2. Department of Computer Science, University of Vermont, 33 Colchester Avenue, Burlington, VT, USA

    Xindong Wu

  3. Computer and Information Science, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

    Erkki Oja

  4. Grupo de Investigación GICAP, Área de Lenguajes y Sistemas Informáticos, Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Burgos, Campus Vena, Francisco de Vitoria, 09006, Burgos, Spain

    Álvaro Herrero  & Bruno Baruque  & 

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Albashiri, K.A., Coenen, F. (2009). A Generic and Extendible Multi-Agent Data Mining Framework. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds) Hybrid Artificial Intelligence Systems. HAIS 2009. Lecture Notes in Computer Science(), vol 5572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02319-4_24

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  • DOI: https://doi.org/10.1007/978-3-642-02319-4_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02318-7

  • Online ISBN: 978-3-642-02319-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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