Computer Graphics
TU Braunschweig

Hierarchical Brushing of High-Dimensional Data Sets Using Quality Metrics


Hierarchical Brushing of High-Dimensional Data Sets Using Quality Metrics

In this paper, we present an interactive exploration framework that puts the human-in-the-loop with the application

of quality metrics and brushing techniques for an efficient visual analysis of high-dimensional data sets.

Our approach makes use of the human ability to distinguish interesting structures even within very cluttered projections

of the data and uses quality metrics to guide the user towards such promising projections which would

otherwise be difficult or time-consuming to find. Brushing the data creates new subsets that are ranked again using

quality metrics and recursively analyzed by the user. This creates a human-in-the-loop approach that makes use

of hierarchical brushing and quality metrics to support interactive exploratory analysis of high-dimensional data

sets. We apply our approach to synthetic and real data sets, demonstrating its usefulness


Author(s):Georgia Albuquerque, Martin Eisemann, Thomas Löwe, Marcus Magnor
Published:October 2014
Type:Article in conference proceedings
Book:Proc. Vision, Modeling and Visualization (VMV)
Presented at:Vision, Modeling and Visualization (VMV) 2014
Project(s): Scalable Visual Analytics 

BibTex PDF 



@inproceedings{Albuquerque2014HBH,
  title = {Hierarchical Brushing of High-Dimensional Data Sets Using Quality Metrics},
  author = {Albuquerque, Georgia and Eisemann, Martin and L{\"o}we, Thomas and Magnor, Marcus},
  booktitle = {Proc. Vision, Modeling and Visualization ({VMV})},
  organization = {Eurographics},
  pages = {1--8},
  month = {Oct},
  year = {2014}
}

Authors