Mining Spatial Rules by Finding Empty Intervals in Data

  • Alexandr Savinov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2773)


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.


Association Rule Rule Induction Empty Interval Thematic Property Enumeration District 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Alexandr Savinov
    • 1
  1. 1.Fraunhofer Institute for Autonomous Intelligent SystemsSankt-AugustinGermany

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