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Scalable sparse bayesian network learning for spatial applications

 
: Liebig, T.; Körner, C.; May, M.

:
Postprint urn:nbn:de:0011-n-935573 (291 KByte PDF)
MD5 Fingerprint: 3183dc8d2cb95d443cdfe183f9ec77ff
© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Erstellt am: 23.4.2009


IEEE Computer Society:
ICDMW '08, IEEE International Conference on Data Mining Workshops. Proceedings : Pisa, 15.12.2008
Washington/DC: IEEE Computer Society Press, 2008
ISBN: 978-0-7695-3503-6
S.420-425
International Conference on Data Mining Workshops (ICDMW) <2008, Pisa>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

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.

: http://publica.fraunhofer.de/dokumente/N-93557.html