Estimating the Number of Clusters via Normalized Cluster Instability

We improve existing instability-based methods for the selection of the number of clusters $k$ in cluster analysis by normalizing instability. In contrast to existing instability methods which only perform well for bounded sequences of small $k$, our method performs well across the whole sequence of possible $k$. In addition, we compare for the first time model-based and model-free variants of $k$ selection via cluster instability and find that their performance is similar. We make our method available in the R-package \verb+cstab+.