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dc.contributor.author Santibañez, M.
dc.contributor.author Valdovinos, R. M.
dc.contributor.author Trueba, A.
dc.contributor.author Rendón, E.
dc.contributor.author Alejo, R.
dc.contributor.author Lopez, E
dc.creator Santibañez, M.
dc.creator Valdovinos, R. M.
dc.creator Trueba, A.
dc.creator Rendón, E.
dc.creator Alejo, R.
dc.creator Lopez, E
dc.date.accessioned 2016-05-18T15:42:24Z
dc.date.available 2016-05-18T15:42:24Z
dc.date.issued 24/11/2013
dc.identifier.issn 978-1-4799-2605-3
dc.identifier.uri http://hdl.handle.net/20.500.11799/41336
dc.description.abstract Over tiime, it has been found there is valuable information within the data sets generated into differnt áreas, These large data sets required to be processed with any data mining technique to get the hidden knowledge inside them. Due to nowaday many of data sets are integrated with a big number of instances and they do not have any information that can describe them, is necessary to use data mining methods such as clustering so it can permit to lump together the data according to its chararcteristics. Although there are algoritms that have good results with small or médium size data sets,they can provide poor results when they work with large data sets. Due to above mentioned in this paper we propose to use different cluster validation methods to determine clustering quality, as its analysis, so at the same time to determine in an empiric way the more realiable rates fopr working large data sets. es
dc.language.iso eng es
dc.publisher IEEE es
dc.rights openAccess es
dc.subject data mining,clustering, cluster validation, validation indexes es
dc.title Applicability of cluster validation indexes for large data sets es
dc.type Capítulo de Libro es
dc.provenance Científica es
dc.road Dorada es


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  • Título
  • Applicability of cluster validation indexes for large data sets
  • Autor
  • Santibañez, M.
  • Valdovinos, R. M.
  • Trueba, A.
  • Rendón, E.
  • Alejo, R.
  • Lopez, E
  • Fecha de publicación
  • 24/11/2013
  • Editor
  • IEEE
  • Tipo de documento
  • Capítulo de Libro
  • Palabras clave
  • data mining,clustering, cluster validation, validation indexes
  • Los documentos depositados en el Repositorio Institucional de la Universidad Autónoma del Estado de México se encuentran a disposición en Acceso Abierto bajo la licencia Creative Commons: Atribución-NoComercial-SinDerivar 4.0 Internacional (CC BY-NC-ND 4.0)

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