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.
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Röver, C., Szepannek, G. (2005). Application of a Genetic Algorithm to Variable Selection in Fuzzy Clustering. In: Weihs, C., Gaul, W. (eds) Classification — the Ubiquitous Challenge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28084-7_80
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DOI: https://doi.org/10.1007/3-540-28084-7_80
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