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Frequency-Incorporated Interdependency Rules Mining in Spatiotemporal Databases

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Book cover Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3213))

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Abstract

Spatiotemporal association rules mining is to reveal interrelationships within large spatiotemporal databases. One critical limitation of traditional approaches is that they are confined to qualitative attribute measures. Quantitative frequencies are either ignored or discretized. In this paper, we propose a robust data mining method that efficiently reveals frequency-incorporated associations in spatiotemporal databases.

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References

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Author information

Authors and Affiliations

  1. School of Information Technology, James Cook University, Douglas Campus, Townsville, QLD 4811, Australia

    Ickjai Lee

Authors
  1. Ickjai Lee
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Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Lee, I. (2004). Frequency-Incorporated Interdependency Rules Mining in Spatiotemporal Databases. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_31

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  • DOI: https://doi.org/10.1007/978-3-540-30132-5_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23318-3

  • Online ISBN: 978-3-540-30132-5

  • eBook Packages: Springer Book Archive

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