Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering
By Wei Peng, Matthew O. Ward, Elke A. Rundensteiner. IEEE Information Visualization Conference, 2004
356 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/d10bebd414e3d9bbc197deb904f94e561edc0069
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Niklas Elmqvist
- Jean-Daniel Fekete
- Pierre Dragicevic
- Geoffrey P. Ellis
- Alan John Dix
- Daniel A. Keim
- Enrico Bertini
- Andrada Tatu
Citations in the corpus
- Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation (2008, background): “[24] generalize this to study visual clutter for a number of visualization techniques—scatterplots being one—and derive visualization-specific clutter measures.”
- A Taxonomy of Clutter Reduction for Information Visualisation (2007, methodology): “…avoid overwhelming the whole view by only showing additional detail within the lens [45], reduce distracting outliers which often confuse the user [38] and automatic clutter reduction reduces the need for user interaction [19, 6] were specific to one or two applications and hence have not been…”
- Empirical Studies in Information Visualization: Seven Scenarios (2012, unknown): “[56] test alternative heuristic search strategies for their axes reordering procedures and run a number of experiments to see how their behavior changes on different data sizes.”
- Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization (2011, methodology): “Later, the word quality metrics assumed a more sp cific meaning; in particular it appeared in the context of a number of papers related to clutter reduction and scalability [9, 10, 28, 30, 39].”
- Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines (2010, background): “New visualization techniques, such as dense pixel displays [52], have been proposed for dealing with large datasets, but most of these approaches still attempt to draw each item in the dataset.”