New aspects of the elastic net algorithm for cluster analysis

datacite.alternateIdentifier.citationNeural Computing and Applications , Vol. 20, N°6, 835-850, 2011es
datacite.alternateIdentifier.citationNeural Computing and Applications, 20 (6), 850-835, 2010
datacite.alternateIdentifier.doi10.1007/s00521-010-0498-x
datacite.alternateIdentifier.issn0941-0643
datacite.creatorLévano, Marcos
datacite.creatorNowak, Hans
datacite.date2010
datacite.date.issued2012-02-06
datacite.rightsAcceso Restringido
datacite.subjectMecánica estadística
datacite.subjectRecocido determinista
datacite.subjectAlgoritmoses
datacite.subjectClústeres de red elástica
datacite.subjectRed elásticaes
datacite.subjectImagenología médica
datacite.subjectMecánica estadísticaes
datacite.subject.englishMecánica estadística
datacite.subject.englishRecocido determinista
datacite.subject.englishClústeres de red elástica
datacite.subject.englishImagenología médica
datacite.titleNew aspects of the elastic net algorithm for cluster analysis
dc.description.abstractThe elastic net algorithm formulated by Durbin Willshaw as a heuristic method and initially applied to solve the traveling salesman problem can be used as a tool for data clustering in n-dimensional space. With the help of statistical mechanics, it is formulated as a deterministic annealing method, where a chain with a fixed number of nodes interacts at different temperatures with the data cloud. From a given temperature on the nodes are found to be the optimal centroids of fuzzy clusters, if the number of nodes is much smaller than the number of data points. We show in this contribution that for this temperature, the centroids of hard clusters, defined by the nearest neighbor clusters of every node, are in the same position as the optimal centroids of the fuzzy clusters. The same is true for the standard deviations. This result can be used as a stopping criterion for the annealing process. The stopping temperature and the number and sizes of the hard clusters depend on the number of nodes in the chain. Test was made with homogeneous and nonhomogeneous artificial clusters in two dimensions. A medical application is given to localize tumors and their size in images of a combined measurement of X-ray computed tomography and positron emission tomography.
dc.formatPDFes
dc.identifier.issn0941-0643
dc.language.isoen
dc.language.isoenes
dc.publisherSpringer
dc.rightsObra bajo licencia Creative Commons Atribución-No Comercial 4.0 Internacional
dc.rights.licensehttps://creativecommons.org/licenses/by-nc/4.0/
dc.sourceNeural Computing and Applications
dc.sourceNeural Computing and Applicationses
dspace.entity.typePublication
oaire.citationEdition2010
oaire.citationEndPage850
oaire.citationIssue6
oaire.citationStartPage835
oaire.citationTitleNeural Computing and Applications
oaire.citationVolume20
oaire.fundingReferenceDirección General de Investigación y Postgrado, Universidad Católica de Temuco- VIP?UCT
oaire.resourceTypeArtículo
oaire.resourceTypeArtículoes
oaire.resourceType.enArticle
uct.carreraIngeniería Civil Industriales
uct.carreraPlan Común Ingenieríaes
uct.catalogadorjvu
uct.comunidadIngenieríaen_US
uct.departamentoDepartamento de Ingeniería Informática
uct.facultadFacultad de Ingeniería
uct.indizacionScience Citation Index Expanded - SCIE
uct.indizacionSCOPUS
uct.indizacionWOS
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