Discussing cluster shapes of fuzzy classifiers
Andreas Nürnberger, Aljoscha Klose, Rudolf Kruse · 2003
Fuzzy classification rules are widely considered a well-suited representation of classification knowledge, as they allow readable and interpretable rule bases. The goal of the paper is to discuss the shapes of the resulting classification borders and thus which class distributions can be represented by such classification systems. 2D and 3D visualizations are used to illustrate the cluster shapes and the borders between distinct classes. Furthermore, general hints concerning the shape of higher dimensional clusters are given.