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Spatial data mining in practice

Principles and case studies
 
: Körner, C.; Hecker, D.; Krause-Traudes, M.; May, M.; Scheider, S.; Schulz, D.; Stange, H.; Wrobel, S.

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Preprint urn:nbn:de:0011-n-1463580 (1.2 MByte PDF)
MD5 Fingerprint: 4aff75a5dbf6c5c048f288abf47f0c7a
Created on: 7.12.2010


Soares, C.; Ghani, Rayid:
Data mining for business applications
Amsterdam: IOS Press, 2010 (Frontiers in Artificial Intelligence and Applications 218)
ISBN: 978-1-60750-632-4 (Print)
ISBN: 978-1-60750-633-1 (Online)
pp.164-178
English
Book Article, Electronic Publication
Fraunhofer IAIS ()
spatial data mining; algorithm; case study

Abstract
Almost any data can be referenced in geographic space. Such data permit advanced analyses that utilize the position and relationships of objects in space as well as geographic background information. Even though spatial data mining is still a young research discipline, in the past years research advances have shown that the particular challenges of spatial data can be mastered and that the technology is ready for practical application when spatial aspects are treated as an integrated part of data mining and model building. In this chapter in particular, we give a detailed description of several customer projects that we have carried out and which all involve customized data mining solutions for business relevant tasks. The applications range from customer segmentation to the prediction of traffic frequencies and the analysis of GPS trajectories. They have been selected to demonstrate key challenges, to provide advanced solutions and to arouse further research questions.

: http://publica.fraunhofer.de/documents/N-146358.html