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dc.contributor.authorDe Paz, Juan F. 
dc.contributor.authorRodríguez González, Sara 
dc.contributor.authorBajo Pérez, Javier
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.contributor.authorLópez, Vivian
dc.date.accessioned2017-09-06T09:15:14Z
dc.date.available2017-09-06T09:15:14Z
dc.date.issued2009
dc.identifier.citationBio-Inspired Systems: Computational and Ambient Intelligence Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 5517, pp. 220-227.
dc.identifier.isbn978-3-642-02477-1 (Print) / 978-3-642-02478-8 (Online)
dc.identifier.issn0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/134960
dc.description.abstractCluster analysis is a technique used in a variety of fields. There are currently various algorithms used for grouping elements that are based on different methods including partitional, hierarchical, density studies, probabilistic, etc. This article will present the SODTNN, which can perform clustering by integrating hierarchical and density-based methods. The network incorporates the behavior of self-organizing maps and does not specify the number of existing clusters in order to create the various groups.
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.titleSelf Organized Dynamic Tree Neural Network
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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