Annealed k-means clustering and decision trees

This paper describes a contribution to the GfKl 2004 Contest. The contest task is to cluster, classify and interpret the 170 districts of the city of Dortmund with respect to their 'social milieux'. A data set containing 204 variables measured for every district is given. We apply annealed k-means clustering to the preprocessed contest data. Superparamagnetic clustering is used to foster insight into the natural partitions of the data. A stable and interpretable solution is obtained with k = 3 clusters, dividing Dortmund into three social milieux. A decision tree is deduced from this cluster solution and is used for interpretation and rule generation. The tree offers the possibility to monitor and predict future assessments. To gain information about cluster solutions with k > 3 a stability analysis based on a resampling approach is performed resulting in further interesting insights.