Resource title

Application of a Genetic Algorithm to Variable Selection in Fuzzy Clustering

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Resource description

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.

Resource author

Christian Röver, Gero Szepannek

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Resource publish date

Resource language

eng

Resource content type

text/html

Resource resource URL

http://hdl.handle.net/10419/22589

Resource license

Adapt according to the presented license agreement and reference the original author.