skip to main content
10.1145/1133265.1133318acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaviConference Proceedingsconference-collections
Article

The plot, the clutter, the sampling and its lens: occlusion measures for automatic clutter reduction

Published: 23 May 2006 Publication History

Abstract

Previous work has demonstrated the use of random sampling in visualising large data sets and the practicality of a sampling lens in enabling focus+context viewing. Autosampling was proposed as a mechanism to maintain constant density within the lens without user intervention. However, this requires rapid calculation of density or clutter. This paper defines clutter in terms of the occlusion of plotted points and evaluates three possible occlusion metrics that can be used with parallel coordinate plots. An empirical study showed the relationship between these metrics was independent of location and could be explained with a surprisingly simple probabilistic model.

References

[1]
Bertini, E. and Santucci, G. Improving 2D scatterplots effectiveness through sampling, displacement and user perception. Proceedings of Information Visualisation 2005, London, July 2005, IEEE
[2]
Bier, E A., Stone, M C., Pier, K., Buxton, W., De Rose, T D. Toolglass and magic lenses: the see-through interface. Proceedings of Computer Graphics and Interactive Techniques, 1993, 73--80
[3]
Brath, R. Concept Demonstration: Metrics for Effective Information Visualization. Symposium on Information Visualization, Phoenix, AZ, Oct 1997, IEEE, 108--111
[4]
Dix, A. and Ellis, G. P. by chance: enhancing interaction with large data sets through statistical sampling. Proceedings of the International Working Conference on Advanced Visual Interfaces, L'Aquila, Italy, May 2002, ACM Press, 167--176
[5]
Ellis, G. P. and Dix, A. Density control through random sampling: an architectural perspective. Proceedings of Information Visualisation 2002, London, July 2002, IEEE, 82--90
[6]
Ellis, G. P., Bertini, E. and Dix, A. The Sampling Lens: Making Sense of Saturated Visualisations. CHI '05 Extended Abstracts on Human Factors in Computing Systems, Portland, USA, 2005, ACM Press, 1351--1354
[7]
Frank, A. U. and Timpf, S. Multiple Representations for Cartographic Objects in a Multi-scale Tree -- An Intelligent Graphical Zoom. Computers and Graphics, 18(6), 1994, 823--829
[8]
Miller, J. and Wegman, E. Construction of line densities for parallel coordinate plots. Computing and Graphics in Statistics, IMA Volumes In Mathematics And Its Applications, 1992, Springer-Verlag, 107--123.
[9]
Rosenholtz, R., Yuanzhen Li, Jonathan Mansfield, Zhenlan Jin. Feature Congestion: A Measure of Display Clutter. Proceedings of the SIGCHI conference on Human factors in computing systems, Apr 2005, ACM Press, 761--770
[10]
Tufte, E. R. The Visual Display of Quantitative Information. Graphics Press, Cheshire, CT, 1983

Cited By

View all
  • (2024)AutoEDA: Iterative Data Focusing and Exploratory Analysis Based on Attribute Frequency2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10832030(4113-4118)Online publication date: 6-Oct-2024
  • (2022)RankAxis: Towards a Systematic Combination of Projection and Ranking in Multi-Attribute Data ExplorationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209463(1-11)Online publication date: 2022
  • (2022)DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential PrivacyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209391(1-11)Online publication date: 2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
AVI '06: Proceedings of the working conference on Advanced visual interfaces
May 2006
512 pages
ISBN:1595933530
DOI:10.1145/1133265
  • General Chair:
  • Augusto Celentano
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 May 2006

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. clutter
  2. density reduction
  3. information visualisation
  4. lens
  5. occlusion
  6. overplotting
  7. random sampling
  8. sampling

Qualifiers

  • Article

Conference

AVI06

Acceptance Rates

Overall Acceptance Rate 128 of 490 submissions, 26%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)6
  • Downloads (Last 6 weeks)1
Reflects downloads up to 09 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)AutoEDA: Iterative Data Focusing and Exploratory Analysis Based on Attribute Frequency2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10832030(4113-4118)Online publication date: 6-Oct-2024
  • (2022)RankAxis: Towards a Systematic Combination of Projection and Ranking in Multi-Attribute Data ExplorationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209463(1-11)Online publication date: 2022
  • (2022)DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential PrivacyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209391(1-11)Online publication date: 2022
  • (2022)Personalized Graph Summarization: Formulation, Scalable Algorithms, and Applications2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00219(2319-2332)Online publication date: May-2022
  • (2022)Visual abstraction of dynamic network via improved multi-class blue noise samplingFrontiers of Computer Science10.1007/s11704-021-0609-017:1Online publication date: 8-Aug-2022
  • (2020)Discriminability Tests for Visualization Effectiveness and ScalabilityIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.293443226:1(749-758)Online publication date: Jan-2020
  • (2019)GeoGate: Correlating Geo-Temporal Datasets Using an Augmented Reality Space-Time Cube and Tangible Interactions2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)10.1109/VR.2019.8797812(210-219)Online publication date: Mar-2019
  • (2019)Visualization and Visual Analytic Techniques for PatternsHigh-Utility Pattern Mining10.1007/978-3-030-04921-8_12(303-337)Online publication date: 19-Jan-2019
  • (2018)Quality Metrics for Information VisualizationComputer Graphics Forum10.1111/cgf.1344637:3(625-662)Online publication date: 10-Jul-2018
  • (2018)SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach2018 IEEE Conference on Visual Analytics Science and Technology (VAST)10.1109/VAST.2018.8802486(36-47)Online publication date: Oct-2018
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media