Authors: Röver, Christian
Szepannek, Gero
Title: Application of a Genetic Algorithm to Variable Selection in Fuzzy Clustering
Language (ISO): en
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.
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.
URI: http://hdl.handle.net/2003/20090
http://dx.doi.org/10.17877/DE290R-15682
Issue Date: 2004
Publisher: Universität Dortmund
Appears in Collections:Sonderforschungsbereich (SFB) 475

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