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Batch self-organizing maps based on city-block distances for interval variables

dc.contributor.authorDe Melo, Filipe M.
dc.contributor.authorBertrand, Patrice
dc.contributor.authorDe A. T. De Carvalho, Francisco
dc.date.accessioned2012-07-04T13:46:54Z
dc.date.available2012-07-04T13:46:54Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/9692
dc.language.isoenen
dc.subjectAdaptive distancesen
dc.subjectCity-block distancesen
dc.subjectInterval-valued dataen
dc.subjectSelf-organizing mapsen
dc.subject.ddc519en
dc.titleBatch self-organizing maps based on city-block distances for interval variablesen
dc.typeDocument de travail / Working paper
dc.contributor.editoruniversityotherCentro de Informática (CIN) Universidade Federal de Pernambuco;Brésil
dc.contributor.editoruniversityotherCentro de Informatica UFPE (CIn) http://www2.cin.ufpe.br/site/index.php Universidade Federal de Pernambuco;Brésil
dc.description.abstractenThe Kohonen Self Organizing Map (SOM) is an unsupervised neural network method with a competitive learning strategy which has both clustering and visualization properties. Interval-valued data arise in practical situations such as recording monthly interval temperatures at meteorological stations, daily interval stock prices, etc. Batch SOM algorithms based on adaptive and non-adaptive city-block distances, suitable for objects described by interval-valued variables, that, for a fixed epoch, optimizes a cost function, are presented. The performance, robustness and usefulness of these SOM algorithms are illustrated with real interval-valued data sets.en
dc.publisher.nameUniversité Paris-Dauphineen
dc.publisher.cityParisen
dc.identifier.citationpages15en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00706519en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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