Abstract
We combine correspondence analysis (CA) and K-means clustering to divide Dortmund's districts into groups that are associated to particular variables and thus represent a social cluster. CA visualizes associations between rows and columns of a frequency matrix and can be used for dimension reduction. Based on the first three dimensions after CA mapping we find a stable partition into five clusters. We further identify variables that are highly associated with the cluster centroids and thus represent a cluster's social condition.
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© 2005 Springer-Verlag Berlin · Heidelberg
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Scheid, S. (2005). Correspondence Clustering of Dortmund City Districts. 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_82
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DOI: https://doi.org/10.1007/3-540-28084-7_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25677-9
Online ISBN: 978-3-540-28084-2
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