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Digital Library of the
European Council for Modelling and Simulation |
Title: |
An Algorithm For Spatial Data Classification And Automatic Mapping
Based On "Spin" Correlations |
Authors: |
Milan Žukovič, Dionissios T. Hristopulos |
Published in: |
ECMS
2008 Proceedings Edited
by: Loucas S. Louca, Yiorgos Chrysanthou, Zuzana Oplatkova, Khalid Al-Begain ISBN:
978-0-9553018-6-5 Doi: 10.7148/2008 22nd
European Conference on Modelling and Simulation, Nicosia, June
3-6, 2008 |
Citation
format: |
Zukovic, M., & Hristopulos,
D. T. (2008). An Algorithm For Spatial Data Classification And Automatic
Mapping Based On “Spin” Correlations. ECMS 2008 Proceedings edited by: L. S. Louca, Y. Chrysanthou, Z. Oplatkova, K. Al-Begain
(pp. 306-312). European Council for Modeling and Simulation. doi:10.7148/2008-0306 |
DOI: |
http://dx.doi.org/10.7148/2008-0306 |
Abstract: |
We employ
the Ising model from statistical physics in the
problem of spatial data classification. We use a multiple- class discretization of the sample values. The proposed
algorithm predicts the class identity at unmeasured points based on Monte
Carlo simulations that are conditional on the observed data (sample). The
algorithm aims to mini- mize the deviation between
the normalized correlation en- ergies of the sample
and the entire domain. A hierarchi- cal scheme is
used, in which points predicted to belong in lower-level classes retain their
identity in the inference of the higher-level classes. The method is
non-parametric and thus suitable for application to non-Gaussian data. The
method is investigated using real data of surface elevation over a large part
of the territory of North America. The ef- fects of the ratio of training to prediction points, the
number of classes, and the initial conditions are investigated. Po- tential extensions of the model are also discussed. |
Full
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