Flexible Clustering

Flexibility of cluster analysis is sometimes understood as the robustness of final partition of objects to the changes in the list of diagnostic variables—deleting some from the list or adding some. In this paper, we propose a procedure which makes possible to calculate a distance matrix on the basis of different subsets of variables, but the selection of variables is somehow unified. The procedure starts with the classical standardization of each variable. Before the calculation of a distance between two objects, we eliminate the variables with the largest absolute value in the first object and in the second object. If by chance the same variable is pointed for elimination for both objects, the next variable with the largest absolute value (for both objects) should be eliminated. With this procedure, each element of the distance matrix is based on the same number of variables, but the variables can be different. As an example, a data set of 17 variables describing human smart society characteristics for 28 European Union countries is used.