• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. Scalable sparse bayesian network learning for spatial applications
 
  • Details
  • Full
Options
2008
Conference Paper
Title

Scalable sparse bayesian network learning for spatial applications

Abstract
Traffic routes through a street network contain patterns and are no random walks. Such patterns exist for instance along streets or between neighbouring street segments. The extraction of these patterns is a challenging task due to the enormous size of city street networks, the large number of required training data and the unknown distribution of the latter. We apply Bayesian Networks to model the correlations between the locations in space-time trajectories and address the following tasks. We introduce and examine a Bayesian Network Learning algorithm enabling us to handle the complexity and performance requirements of the spatial context. Furthermore, we apply our method to German cities, evaluate the accuracy and analyse the runtime behaviour for different parameter settings.
Author(s)
Liebig, Thomas  
Körner, Christine  
May, Michael  
Mainwork
ICDMW '08, IEEE International Conference on Data Mining Workshops. Proceedings  
Conference
International Conference on Data Mining Workshops (ICDMW) 2008  
Open Access
File(s)
Download (245.55 KB)
Rights
Use according to copyright law
DOI
10.24406/publica-r-359900
10.1109/ICDMW.2008.124
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024