Centroid-based partitioning cluster analysis is a popular method for
segmenting data into more homogeneous subgroups. Visualization can
help tremendously to understand the positions of these subgroups
relative to each other in higher dimensional spaces and to assess
the quality of partitions. In this paper we present several
improvements on existing cluster displays using neighborhood graphs
with edge weights based on cluster separation and convex hulls of
inner and outer cluster regions. A new display called shadow-stars
can be used to diagnose pairwise cluster separation with respect to
the distribution of the original data. Artificial data and two case
studies with real data are used to demonstrate the techniques
