A Taxonomy of Clutter Reduction for Information Visualisation

By Geoffrey P. Ellis, Alan John Dix. IEEE Transactions on Visualization and Computer Graphics, 2007

453 citations recorded by the source corpus.

Information visualisation is about gaining insight into data through a visual representation. This data is often multivariate and increasingly, the datasets are very large. To help us explore all this data, numerous visualisation applications, both commercial and research prototypes, have been designed using a variety of techniques and algorithms. Whether they are dedicated to geo-spatial data or skewed hierarchical data, most of the visualisations need to adopt strategies for dealing with overcrowded displays, brought about by too much data to fit in too small a display space. This paper analyses a large number of these clutter reduction methods, classifying them both in terms of how they deal with clutter reduction and more importantly, in terms of the benefits and losses. The aim of the resulting taxonomy is to act as a guide to match techniques to problems where different criteria may have different importance, and more importantly as a means to critique and hence develop existing and new techniques.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/788c74adcf51c74a92cf4fed44a8dad81f9e8ce7

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Citations in the corpus

Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines (2010, methodology): “More speciﬁcally, some abstraction methods—including ﬁltering [64], clustering [50, 51], and sampling [25]—operate on the actual items in the dataset, whereas others—such as distortion [55] and zooming [58]—affect the visual presentation of the items.”
Geometry-Based Edge Clustering for Graph Visualization (2008, background): “An excellent survey on general clutter reduction techniques can be found in [7].”
A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges (2013, background): “Visual clutter is a commonly-found problem in information visualization([95]).”
Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization (2011, background): “Ellis and Dix organize a large number of existing clutter reduction techniques into a clutt r reduction taxonomy [18].”
Visualizing High-Dimensional Data: Advances in the Past Decade (2017, unknown): “number of surveys have focused on different aspects of highdimensional data visualization, such as parallel coordinates [6], [7], quality measures [8], clutter reduction [9], visual data mining [10], [11], [12], and interactive techniques [13].”
Mastering the Information Age - Solving Problems with Visual Analytics (2010, background): “Visualising large datasets, often produces cluttered images, and hence various clutter reduction Visualising large datasets often produces cluttered images techniques[41] have been developed by the information visualisation community to ameliorate this.”
Design Considerations for Optimizing Storyline Visualizations (2012, background): “A survey of general techniques for reducing visual clutter in visualizations can be found in [9].”
An Information-theoretic Framework for Visualization (2010, methodology): “In practice, we choose to use a graphical (or visual) mapping G(Z) as in Fig.”