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Scattering Points in Parallel Coordinates

Published: 01 November 2009 Publication History

Abstract

In this paper, we present a novel parallel coordinates design integrated with points (Scattering Points in Parallel Coordinates, SPPC), by taking advantage of both parallel coordinates and scatterplots. Different from most multiple views visualization frameworks involving parallel coordinates where each visualization type occupies an individual window, we convert two selected neighboring coordinate axes into a scatterplot directly. Multidimensional scaling is adopted to allow converting multiple axes into a single subplot. The transition between two visual types is designed in a seamless way. In our work, a series of interaction tools has been developed. Uniform brushing functionality is implemented to allow the user to perform data selection on both points and parallel coordinate polylines without explicitly switching tools. A GPU accelerated Dimensional Incremental Multidimensional Scaling (DIMDS) has been developed to significantly improve the system performance. Our case study shows that our scheme is more efficient than traditional multi-view methods in performing visual analysis tasks.

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Published In

cover image IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics  Volume 15, Issue 6
November 2009
4338 pages

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 November 2009

Author Tags

  1. Dimensionality reduction
  2. interactivity
  3. quality metrics
  4. variable ordering.

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  • (2022)Towards better pattern enhancement in temporal evolving set visualizationJournal of Visualization10.1007/s12650-022-00896-x26:3(611-629)Online publication date: 27-Nov-2022
  • (2020)Double-Arc Parallel Coordinates and its Axes re-Ordering MethodsMobile Networks and Applications10.1007/s11036-019-01455-925:4(1376-1391)Online publication date: 1-Aug-2020
  • (2019)Visualisation of Pareto Front Approximation: A Short Survey and Empirical Comparisons2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790298(1750-1757)Online publication date: 10-Jun-2019
  • (2019)A survey on visualization approaches for exploring association relationships in graph dataJournal of Visualization10.1007/s12650-019-00551-y22:3(625-639)Online publication date: 1-Jun-2019
  • (2019)GeoBrickThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-017-1461-y35:2(191-204)Online publication date: 1-Feb-2019
  • (2018)A visual interface for feature subset selection using machine learning methodsProceedings of the XXVIII Spanish Computer Graphics Conference10.2312/ceig.20181165(119-128)Online publication date: 27-Jun-2018
  • (2017)Additional on-demand dimension for data visualizationProceedings of the Eurographics/IEEE VGTC Conference on Visualization: Short Papers10.2312/eurovisshort.20171151(163-167)Online publication date: 12-Jun-2017
  • (2017)Visualizing High-Dimensional DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2016.264096023:3(1249-1268)Online publication date: 1-Mar-2017
  • (2016)Exploring Items and Features with IF, FI-TablesComputer Graphics Forum10.5555/3071534.307154035:3(31-40)Online publication date: 1-Jun-2016
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