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Mining Spatial Rules by Finding Empty Intervals in Data

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Book cover Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2773))

Abstract

Most rule induction algorithms including those for association rule mining use high support as one of the main measures of interestingness. In this paper we follow an opposite approach and describe an algorithm, called Optimist, which finds all largest empty intervals in data and then transforms then into the form of multiple-valued rules. It is demonstrated how this algorithm can be applied to mining spatial rules where data involves both geographic and thematic properties. Data preparation (spatial feature generation), data analysis and knowledge postprocessing stages were implemented in the SPIN! spatial data mining system where this algorithm is one of its components.

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References

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Author information

Authors and Affiliations

  1. Fraunhofer Institute for Autonomous Intelligent Systems, Schloss Birlinghoven, Sankt-Augustin, D-53754, Germany

    Alexandr Savinov

Authors
  1. Alexandr Savinov
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Editor information

Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, United Kingdom

    Vasile Palade

  2. Intelligent Systems and Signal Processing Labs, Moulsecoomb, University of Brighton, BN2 4GJ, Brighton, United Kingdom

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, Mawson Lakes, University of South Australia, 5095, Adelaide, SA, Australia

    Lakhmi Jain

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© 2003 Springer-Verlag Berlin Heidelberg

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Savinov, A. (2003). Mining Spatial Rules by Finding Empty Intervals in Data. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_141

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  • DOI: https://doi.org/10.1007/978-3-540-45224-9_141

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40803-1

  • Online ISBN: 978-3-540-45224-9

  • eBook Packages: Springer Book Archive

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