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Self organizing maps for outlier detection

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorMuñoz, Alberto
dc.contributor.authorMuruzábal, Jorge
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadísticaes
dc.date.accessioned2011-02-23T18:21:19Z
dc.date.available2011-02-23T18:21:19Z
dc.date.issued1995-11
dc.description.abstractIn this paper we address the problem of multivariate outlier detection using the (unsupervised) self-organizing map (SOM) algorithm introduced by Kohonen. We examine a number of techniques, based on summary statistics and graphics derived from the trained SOM, and conclude that they work well in cooperation with each other. Useful tools include the median interneuron distance matrix and the projection ofthe trained map (via Sammon's projection). SOM quantization errors provide an important complementary source of information for certain type of outlying behavior. Empirical results are reported on both artificial and real data.es
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/octet-stream
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10016/10345
dc.language.isoenges
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometricses
dc.relation.ispartofseries95-53es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadísticaes
dc.subject.otherSelf-organizationes
dc.subject.otherAtypical Dataes
dc.subject.otherRobustnesses
dc.subject.otherDimensionality Reductiones
dc.subject.otherNonlinear Projectionses
dc.titleSelf organizing maps for outlier detectiones
dc.typeworking paper*
dspace.entity.typePublication
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