Smart Brushing for Parallel Coordinates

The Parallel Coordinates plot is a popular tool for the visualization of high-dimensional data. One of the main challenges when using parallel coordinates is occlusion and overplotting resulting from large data sets. Brushing is a popular approach to address these challenges. Since its conception, l...

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Détails bibliographiques
Publié dans:IEEE transactions on visualization and computer graphics. - 1996. - 25(2019), 3 vom: 21. März, Seite 1575-1590
Auteur principal: Roberts, Richard C (Auteur)
Autres auteurs: Laramee, Robert S, Smith, Gary A, Brookes, Paul, DCruze, Tony
Format: Article en ligne
Langue:English
Publié: 2019
Accès à la collection:IEEE transactions on visualization and computer graphics
Sujets:Journal Article
Description
Résumé:The Parallel Coordinates plot is a popular tool for the visualization of high-dimensional data. One of the main challenges when using parallel coordinates is occlusion and overplotting resulting from large data sets. Brushing is a popular approach to address these challenges. Since its conception, limited improvements have been made to brushing both in the form of visual design and functional interaction. We present a set of novel, smart brushing techniques that enhance the standard interactive brushing of a parallel coordinates plot. We introduce two new interaction concepts: Higher-order, sketch-based brushing, and smart, data-driven brushing. Higher-order brushes support interactive, flexible, n-dimensional pattern searches involving an arbitrary number of dimensions. Smart, data-driven brushing provides interactive, real-time guidance to the user during the brushing process based on derived meta-data. In addition, we implement a selection of novel enhancements and user options that complement the two techniques as well as enhance the exploration and analytical ability of the user. We demonstrate the utility and evaluate the results using a case study with a large, high-dimensional, real-world telecommunication data set and we report domain expert feedback from the data suppliers
Description:Date Revised 20.11.2019
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1941-0506
DOI:10.1109/TVCG.2018.2808969