Spatial Outlier Detection: Data, Algorithms, Visualizations

  • Elke Achtert
  • Ahmed Hettab
  • Hans-Peter Kriegel
  • Erich Schubert
  • Arthur Zimek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6849)


Current Geographic/Geospatial Information Systems (GIS) and Data Mining Systems (DMS) so far are usually not designed to interoperate. GIS research has a strong emphasis on information management and retrieval, whereas DMS usually have too little geographic functionality to perform appropriate analysis. In this demonstration, we introduce an integrated GIS-DMS system for performing advanced data mining tasks such as outlier detection on geo-spatial data, but which also allows the interaction with existing GIS and this way allows a thorough evaluation of the results. The system enables convenient development of new algorithms as well as application of existing data mining algorithms to the spatial domain, bridging the gap between these two worlds.


Outlier Detection Subspace Cluster Open Geospatial Consortium Spatial Outlier Geography Markup Language 
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 2011

Authors and Affiliations

  • Elke Achtert
    • 1
  • Ahmed Hettab
    • 1
  • Hans-Peter Kriegel
    • 1
  • Erich Schubert
    • 1
  • Arthur Zimek
    • 1
  1. 1.Ludwig-Maximilians-Universität MünchenMünchenGermany

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