Cluster analysis

segmentation as a research question; hierarchical clustering, the dendrogram and how to read a cut; K-means and how to choose k; two-step clustering and why it handles mixed variable types; standardising variables before clustering and why it is not optional; distance measures; profiling and naming the resulting clusters; validating a solution; the honest caveat that cluster analysis always returns clusters, whether or not they exist.