Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach

  • Michelangelo Ceci
  • Annalisa Appice
  • Donato Malerba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4702)


Spatial Data Mining (SDM) has great potential in supporting public policy and in underpinning society functioning. One task in SDM is the discovery of characterization and peculiarities of communities sharing socio-economic aspects in order to identify potentialities, needs and public intervention. Emerging patterns (EPs) are a special kind of pattern which contrast two classes. In this paper, we face the problem of extracting EPs from spatial data. At this aim, we resort to a multi-relational approach in order to deal with the degree of complexity of discovering EPs from spatial data (i.e., (i) the spatial dimension implicitly defines spatial properties and relations, (ii) spatial phenomena are affected by autocorrelation). Experiments on real datasets are described.


Association Rule Spatial Database Reference Object Spatial Object Relational Pattern 
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 2007

Authors and Affiliations

  • Michelangelo Ceci
    • 1
  • Annalisa Appice
    • 1
  • Donato Malerba
    • 1
  1. 1.Dipartimento di Informatica, Università degli Studi di Bari, via Orabona, 4 - 70126 Bari - Italy 

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