Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22589
Authors: 
Röver, Christian
Szepannek, Gero
Year of Publication: 
2004
Series/Report no.: 
Technical Report / Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2004,76
Abstract: 
In 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.
Document Type: 
Working Paper

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