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Application of a Genetic Algorithm to Variable Selection in Fuzzy Clustering

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  • Röver, Christian
  • Szepannek, Gero

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.

Suggested Citation

  • Röver, Christian & Szepannek, Gero, 2004. "Application of a Genetic Algorithm to Variable Selection in Fuzzy Clustering," Technical Reports 2004,76, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200476
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