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dc.contributor.authorArroyo Puente, Ángel
dc.contributor.authorTricio, Verónica
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.contributor.authorHerrero Cosío, Álvaro
dc.date.accessioned2017-09-06T09:16:03Z
dc.date.available2017-09-06T09:16:03Z
dc.date.issued2015/06
dc.identifier.citation10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing . Volumen 368, pp. 117-130.
dc.identifier.isbn978-3-319-19718-0(Print) / 978-3-319-19719-7(Online)
dc.identifier.issn2194-5357(Print) / 2194-5365(Online)
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-19719-7_11
dc.identifier.urihttp://hdl.handle.net/10366/135047
dc.description.abstractPresent work proposes the application of several clustering techniques (k-means, SOM k-means, k-medoids, and agglomerative hierarchical) to analyze the climatological conditions in different places. To do so, real-life data from data acquisition stations in Spain are analyzed, provided by AEMET (Spanish Meteorological Agency). Some of the main meteorological variables daily acquired by these stations are studied in order to analyse the variability of the environmental conditions in the selected places. Additionally, it is intended to characterize the stations according to their location, which could be applied for any other station. A comprehensive analysis of four different clustering techniques is performed, giving interesting results for a meteorological analysis.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleA Comparison of Clustering Techniques for Meteorological Analysis
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported