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dc.contributor.author | De Paz, Juan F. | |
dc.contributor.author | Rodríguez González, Sara | |
dc.contributor.author | Bajo Pérez, Javier | |
dc.contributor.author | Corchado Rodríguez, Juan Manuel | |
dc.contributor.author | López, Vivian | |
dc.date.accessioned | 2017-09-06T09:15:14Z | |
dc.date.available | 2017-09-06T09:15:14Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | Bio-Inspired Systems: Computational and Ambient Intelligence Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 5517, pp. 220-227. | |
dc.identifier.isbn | 978-3-642-02477-1 (Print) / 978-3-642-02478-8 (Online) | |
dc.identifier.issn | 0302-9743 (Print) / 1611-3349 (Online) | |
dc.identifier.uri | http://hdl.handle.net/10366/134960 | |
dc.description.abstract | Cluster 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.mimetype | application/pdf | |
dc.language.iso | en | |
dc.publisher | Springer Science + Business Media | |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Unported | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/ | |
dc.subject | Computer Science | |
dc.title | Self Organized Dynamic Tree Neural Network | |
dc.type | info:eu-repo/semantics/article | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess |
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