Supporting the Process of Monument Classification Based on Reducts, Decision Rules and Neural Networks

  • Robert Olszewski
  • Anna Fiedukowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)


The present article attempts to support the process of classification of multi-characteristic spatial data in order to develop the correct cartographic visualisation of complex geographical information in the thematic geoportal. Rough sets, decision rules and artificial neural networks were selected as relevant methods of spatially distributed monument classification. Basing on the obtained results it was determined that the attributes reflecting the spatial relations between specific objects play an extremely significant role in the process of classification, reducts allow to select exclusively essential attributes of objects and neural networks and decision rules are highly useful for the purposes of classification of multi-characteristic spatial data.


rough sets neural networks spatial data mining multi-characteristic spatial data classification 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Robert Olszewski
    • 1
  • Anna Fiedukowicz
    • 1
  1. 1.Faculty of Geodesy and Cartography, Department of CartographyWarsaw University of TechnologyWarsawPoland

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