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2008 7th Computer Information Systems and Industrial Management Applications
Self-Organizing Map and K-Means for Meteorological Day Type Identification for the Region of Annaba -Algeria-
June 26-June 28
ISBN: 978-0-7695-3184-7
| ASCII Text | x | ||
| Soufiane Khedairia, Mohamed Tarek Khadir, "Self-Organizing Map and K-Means for Meteorological Day Type Identification for the Region of Annaba -Algeria-," Computer Information Systems and Industrial Management Applications, International Conference on, pp. 91-96, 2008 7th Computer Information Systems and Industrial Management Applications, 2008. | |||
| BibTex | x | ||
| @article{ 10.1109/CISIM.2008.29, author = {Soufiane Khedairia and Mohamed Tarek Khadir}, title = {Self-Organizing Map and K-Means for Meteorological Day Type Identification for the Region of Annaba -Algeria-}, journal ={Computer Information Systems and Industrial Management Applications, International Conference on}, volume = {0}, year = {2008}, isbn = {978-0-7695-3184-7}, pages = {91-96}, doi = {http://doi.ieeecomputersociety.org/10.1109/CISIM.2008.29}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Computer Information Systems and Industrial Management Applications, International Conference on TI - Self-Organizing Map and K-Means for Meteorological Day Type Identification for the Region of Annaba -Algeria- SN - 978-0-7695-3184-7 SP91 EP96 A1 - Soufiane Khedairia, A1 - Mohamed Tarek Khadir, PY - 2008 KW - Meteorological day type identification KW - self-organizing map KW - k-means KW - clustering KW - Kohonen maps VL - 0 JA - Computer Information Systems and Industrial Management Applications, International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CISIM.2008.29
A two level clustering approach has been proposed in this paper in order to perform a classification analysis of meteorological data of Annaba region (North-East of Algeria) using data from 1995 to 1999. The Kohonen self-organizing map (SOM) has been used to group the data and produce the meteorological prototypes. The number of prototypes of SOM is large, to facilitate quantitative analysis of the map and the data similar units need to be grouped (clustered). As a second clustering stage k-means algorithm has been used to cluster the SOM units. Quantitative (using two categories of validity indices) and qualitative criteria were introduced to verify the results of the clustering. The different experiments developed extracted six distinct classes, which were related to typical meteorological conditions in the area.
Index Terms:
Meteorological day type identification, self-organizing map, k-means, clustering, Kohonen maps
Citation:
Soufiane Khedairia, Mohamed Tarek Khadir, "Self-Organizing Map and K-Means for Meteorological Day Type Identification for the Region of Annaba -Algeria-," cisim, pp.91-96, 2008 7th Computer Information Systems and Industrial Management Applications, 2008
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