Cluster analysis is an important data mining technique for analyzing large amounts of data, reducing many
objects to a limited number of clusters. Cluster visualization techniques aim at supporting the user in better
understanding the characteristics and relationships among the found clusters. While promising approaches
to visual cluster analysis already exist, these usually fall short of incorporating the quality of the obtained
clustering results. However, due to the nature of the clustering process, quality plays an important aspect, as
for most practical data sets, typically many different clusterings are possible. Being aware of clustering quality
is important to judge the expressiveness of a given cluster visualization, or to adjust the clustering process with
refined parameters, among others.
In this work, we present an encompassing suite of visual tools for quality assessment of an important visual
cluster algorithm, namely, the Self-Organizing Map (SOM) technique. We define, measure, and visualize the
notion of SOM cluster quality along a hierarchy of cluster abstractions. The quality abstractions range from
simple scalar-valued quality scores up to the structural comparison of a given SOM clustering with output of
additional supportive clustering methods. The suite of methods allows the user to assess the SOM quality on the
appropriate abstraction level, and arrive at improved clustering results. We implement our tools in an integrated
system, apply it on experimental data sets, and show its applicability.