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it2015

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 de- pict 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.

Type: Article

Author: Patrick Oesterling and Patrick Jähnichen and Gerhard Heyer and Gerik Scheuermann
Title: Topological visual analysis of clusterings in high-dimensional information spaces
Journal: it - Information Technology
Year: 2015
Volume:57
Number:1
Pages:3-10
Note:Special Issue: Visual Analytics
@ARTICLE{it2015,
AUTHOR = {Patrick Oesterling and Patrick Jähnichen and Gerhard Heyer and Gerik Scheuermann},
TITLE = {Topological visual analysis of clusterings in high-dimensional information spaces},
JOURNAL = {it - Information Technology},
YEAR = {2015},
VOLUME = {57},
NUMBER = {1},
PAGES = {3-10},
NOTE = {Special Issue: Visual Analytics}
}