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Knowledge Discovery Using Neural Networks

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Innovations in Applied Artificial Intelligence (IEA/AIE 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3029))

Abstract

A novel knowledge discovery technique using neural networks is presented. A neural network is trained to learn the correlations and relationships that exist in a dataset. The neural network is then pruned and modified to generalize the correlations and relationships. Finally, the neural network is used as a tool to discover all existing hidden trends in four different types of crimes in US cities as well as to predict trends based on existing knowledge inherent in the network.

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Author information

Authors and Affiliations

  1. Department of Computer Science Texas, State University, San Marcos, Texas, 78666

    Khosrow Kaikhah & Sandesh Doddameti

Authors
  1. Khosrow Kaikhah
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  2. Sandesh Doddameti
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Editor information

Editors and Affiliations

  1. Institute for Information Technology, National Research Council of Canada, 1200 Montreal Read, M-50, K1A 0R6, Ottawa, Ontario, Canada

    Bob Orchard

  2. Institute for Information Technology, National Research Council, Canada

    Chunsheng Yang

  3. Department of Computer Science, Texas State University-San Marcos, Nueces 247, 601 University Drive, TX 78666-4616, San Marcos, USA

    Moonis Ali

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© 2004 Springer-Verlag Berlin Heidelberg

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Kaikhah, K., Doddameti, S. (2004). Knowledge Discovery Using Neural Networks. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_3

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  • DOI: https://doi.org/10.1007/978-3-540-24677-0_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22007-7

  • Online ISBN: 978-3-540-24677-0

  • eBook Packages: Springer Book Archive

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