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Analyzing Multi-level Spatial Association Rules Through a Graph-Based Visualization

  • Annalisa Appice
  • Paolo Buono
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3533)

Abstract

Association rules discovery is a fundamental task in spatial data mining where data are naturally described at multiple levels of granularity. ARES is a spatial data mining system that takes advantage from this taxonomic knowledge on spatial data to mine multi-level spatial association rules. A large amount of rules is typically discovered even from small set of spatial data. In this paper we present a graph-based visualization that supports data miners in the analysis of multi-level spatial association rules discovered by ARES and takes advantage from hierarchies describing the same spatial object at multiple levels of granularity. An application on real-world spatial data is reported. Results show that the use of the proposed visualization technique is beneficial.

Keywords

Association Rule Spatial Database Granularity Level Spatial Object Regional Road 
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 2005

Authors and Affiliations

  • Annalisa Appice
    • 1
  • Paolo Buono
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di BariItaly

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