Accessible Unlicensed Requires Authentication Published by De Gruyter Oldenbourg January 30, 2015

Topological visual analysis of clusterings in high-dimensional information spaces

Patrick Oesterling, Patrick Jähnichen, Gerhard Heyer and Gerik Scheuermann

Abstract

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.

Received: 2014-7-25
Revised: 2014-10-30
Accepted: 2014-12-5
Published Online: 2015-1-30
Published in Print: 2015-2-28

©2015 Walter de Gruyter Berlin/Boston