A GH-SOM optimization with SOM labelling and dunn index

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dc.contributor.authorBokan Garay, Alessandro
dc.contributor.authorPonce Contreras, Guillermo
dc.contributor.authorPatiño Escarcina, Raquel
dc.date.accessioned2019-01-29T22:19:55Z
dc.date.available2019-01-29T22:19:55Z
dc.date.issued2011
dc.description.abstractClustering is an unsupervised classification method that divides a data set in groups, where the elements of a group have similar characteristics to each other. A well-known clustering method is the Growing Hierarchical Self-Organizing Map (GH-SOM), that improves the results of an ordinary SOM by controlling the number of neurons generated. In this paper it is proposed a optimization of the typical GH-SOM, using a cluster validation index to verify the quality of partitioning. © 2011 IEEE.es_PE
dc.description.uriTrabajo académicoes_PE
dc.identifier.doihttps://doi.org/10.1109/HIS.2011.6122168es_PE
dc.identifier.isbnurn:isbn:9781457721502es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12590/15891
dc.language.isoenges_PE
dc.publisherScopuses_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84856748692&doi=10.1109%2fHIS.2011.6122168&partnerID=40&md5=84d450a8bb887d678d92546afd361157es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectCluster validationes_PE
dc.subjectClustering methodses_PE
dc.subjectData setses_PE
dc.subjectGrowing hierarchical self-organizing mapses_PE
dc.subjectUnsupervised classificationes_PE
dc.subjectIntelligent systemses_PE
dc.subjectOptimizationes_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00es_PE
dc.titleA GH-SOM optimization with SOM labelling and dunn indexes_PE
dc.typeinfo:eu-repo/semantics/conferenceObject
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