Automated analytical methods to support visual exploration of high-dimensional data

Andrada Tatu, Georgia Albuquerque, Martin Eisemann, Peter Bak, Holger Theisel, Marcus Magnor, Daniel Keim

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


Visual exploration of multivariate data typically requires projection onto lower dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even unfeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be used as a starting point for interactive data analysis. This can effectively ease the task of finding truly useful visualizations and potentially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non-class-based scatterplots and parallel coordinates visualizations. The proposed analysis methods are evaluated on different data sets.

Original languageEnglish
Article number5620902
Pages (from-to)584-597
Number of pages14
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number5
StatePublished - 14 Jan 2011
Externally publishedYes


  • Dimensionality reduction
  • parallel coordinates
  • quality measures
  • scatterplots

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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