In many applications, domain-specific entities are easily compared and
categorized if they are represented as high-dimensional feature
vectors. To detect object similarities and to quantify coherent
groups, analysts often visualize the vectors directly, aiming to
identify clusters visually. However, common visualizations for
high-dimensional data often suffer from information loss, occlusions
and visual clutter for large and noisy data. In this case, structure
is misleading and false insights are derived. We use topological
concepts to provide a structural view of the points. We analyze them
in their original space and depict their clustering structure using
intuitive landscapes. We describe the visual analysis process to
define and simplify the structural view and to perform local analysis
by linking individual features to other visualizations.
it - Information Technology is a strictly peer-reviewed scientific journal. It is the oldest German journal in the field of information technology. Today, the major aim of it - Information Technology is highlighting issues on ongoing newsworthy areas in information technology and informatics and their application. It aims at presenting the topics with a holistic view.