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.
The analysis of high-dimensional data is an important, yet inherently difficult problem. Projection techniques
such as PCA, MDS, and SOM can be used to map high-dimensional data to 2D display space. However,
projections typically incur a loss in information. Often uncertainty exists regarding the precision of the projection
as compared with its original data characteristics. While the output quality of these projection techniques can be
discussed in terms of algorithmic assessment, visualization is often helpful for better understanding the results.
We address the visual assessment of projection precision by an approach integrating an appropriately designed
projection precision measure directly into the projection visualization. To this end, a flexible projection precision
measure is defined that allows the user to balance the degree of locality at which the measure is evaluated. Several
visual mappings are designed for integrating the precision measure into the projection visualization at various
levels of abstraction. The techniques are implemented in a fully interactive system which is practically applied
on several data sets. We demonstrate the usefulness of the approach for visual analysis of classified and clustered
high-dimensional data sets. We thereby show how our novel interactive precision quality visualization system
helps to examine preservation of closeness of the data in original space into the low-dimensional space.
The analysis of large corporate shareholder network structures is an important task in corporate governance, in
financing, and in financial investment domains. In a modern economy, large structures of cross-corporation, cross-border
shareholder relationships exist, forming complex networks. These networks are often difficult to analyze with traditional
approaches. An efficient visualization of the networks helps to reveal the interdependent shareholding formations and
the controlling patterns. In this paper, we propose an effective visualization tool that supports the financial analyst in
understanding complex shareholding networks. We develop an interactive visual analysis system by combining state-of-the-art visualization technologies with economic analysis methods. Our system is capable to reveal patterns in large
corporate shareholder networks, allows the visual identification of the ultimate shareholders, and supports the visual
analysis of integrated cash flow and control rights. We apply our system on an extensive real-world database of
shareholder relationships, showing its usefulness for effective visual analysis.