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dc.contributor.authorde Paz Santana, Juan F.
dc.contributor.authorRodríguez González, Sara 
dc.contributor.authorBajo Pérez, Javier
dc.contributor.authorCorchado Rodríguez, Juan M.
dc.date.accessioned2017-09-06T09:15:23Z
dc.date.available2017-09-06T09:15:23Z
dc.date.issued2009
dc.identifier.citation9th Computational and Mathematical Methods in Science and Engineering. Volumen 4.
dc.identifier.urihttp://hdl.handle.net/10366/134977
dc.description.abstractClustering is a branch of multivariate analysis that is used to create groups of data. While there are currently a variety of techniques that are used for creating clusters, many require defining additional information, including the actual number of clusters, before they can be carried out. The case study of this research presents a novel neural network that is capable of creating groups by using a combination of hierarchical clustering and self-organizing maps, without requiring the number of existing clusters to be specified beforehand.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherJ. Vigo Aguilar, P. Alonso, S. Oharu, E. Venturino and B. Wade.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleA new clustegin algorithm applying a hierarchical method neural network
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