Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines
By Niklas Elmqvist, Jean-Daniel Fekete. 2010
452 citations recorded by the source corpus.
We present a model for building, visualizing, and interacting with multiscale representations of information visualization techniques using hierarchical aggregation. The motivation for this work is to make visual representations more visually scalable and less cluttered. The model allows for augmenting existing techniques with multiscale functionality, as well as for designing new visualization and interaction techniques that conform to this new class of visual representations. We give some examples of how to use the model for standard information visualization techniques such as scatterplots, parallel coordinates, and node-link diagrams, and discuss existing techniques that are based on hierarchical aggregation. This yields a set of design guidelines for aggregated visualizations. We also present a basic vocabulary of interaction techniques suitable for navigating these multiscale visualizations.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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Citations in the corpus
- Towards Better Analysis of Deep Convolutional Neural Networks (2016, methodology): “Possible solutions are to employ the range traversal technique [16] or provide additional visual hint(s).”
- Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities (2016, methodology): “• node-link diagrams (Elmqvist and Fekete, 2010) with graph layout algorithms such as Reingold and TIlford, Htrees and Balloon graphs (Herman et al.”