Self organizing maps for outlier detection

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dc.contributor.author Muñoz, Alberto
dc.contributor.author Muruzábal, Jorge
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2011-02-23T18:21:19Z
dc.date.available 2011-02-23T18:21:19Z
dc.date.issued 1995-11
dc.identifier.uri http://hdl.handle.net/10016/10345
dc.description.abstract In 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.
dc.format.mimetype application/octet-stream
dc.format.mimetype application/octet-stream
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 95-53
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Self-organization
dc.subject.other Atypical Data
dc.subject.other Robustness
dc.subject.other Dimensionality Reduction
dc.subject.other Nonlinear Projections
dc.title Self organizing maps for outlier detection
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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