Full metadata record
DC FieldValueLanguage
dc.contributor.authorRöver, Christiande
dc.contributor.authorSzepannek, Gerode
dc.date.accessioned2005-01-31T08:15:33Z-
dc.date.available2005-01-31T08:15:33Z-
dc.date.issued2004de
dc.identifier.urihttp://hdl.handle.net/2003/20090-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15682-
dc.description.abstractIn order to group the observations of a data set into a given number of clusters, an optimal subset out of a greater number of explanatory variables is to be selected. The problem is approached by maximizing a quality measure under certain restrictions that are supposed to keep the subset most representative of the whole data. The restrictions may either be set manually, or generated from the data. A genetic optimization algorithm is developed to solve this problem. The procedure is then applied to a data set describing features of sub-districts of the city of Dortmund, Germany, to detect different social milieus and investigate the variables making up the differences between these.de
dc.description.abstractIn order to group the observations of a data set into a given number of clusters, an ‘optimal’ subset out of a greater number of explanatory variables is to be selected. The problem is approached by maximizing a quality measure under certain restrictions that are supposed to keep the subset most representative of the whole data. The restrictions may either be set manually, or generated from the data. A genetic optimization algorithm is developed to solve this problem. The procedure is then applied to a data set describing features of sub-districts of the city of Dortmund, Germany, to detect different social milieus and investigate the variables making up the differences between these.en
dc.format.extent135001 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoende
dc.publisherUniversität Dortmundde
dc.subject.ddc310de
dc.titleApplication of a Genetic Algorithm to Variable Selection in Fuzzy Clusteringen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

Files in This Item:
File Description SizeFormat 
76_04.pdfDNB131.84 kBAdobe PDFView/Open


This item is protected by original copyright



This item is protected by original copyright rightsstatements.org