The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization
Abstract
In this paper, we present a systematization of techniques that use quality metrics to help in the visual exploration of meaningful patterns in high-dimensional data. In a number of recent papers, different quality metrics are proposed to automate the demanding search through large spaces of alternative visualizations (e.g., alternative projections or ordering), allowing the user to concentrate on the most promising visualizations suggested by the quality metrics. Over the last decade, this approach has witnessed a remarkable development but few reflections exist on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a thorough literature review. We carefully analyze the papers and derive a set of factors for discriminating the quality metrics, visualization techniques, and the process itself. The process is described through a reworked version of the well-known information visualization pipeline. We demonstrate the usefulness of our model by applying it to several existing approaches that use quality metrics, and we provide reflections on implications of our model for future research.
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Cited by
Visualizing structure and transitions in high-dimensional biological data
Moon, K. R., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D. B., Chen, W. S., Yim, K., Elzen, A. V. D., Hirn, M. J., Coifman, R. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0336-3 · Cited by 1,065
Visualizing high-dimensional data: advances in the past decade
Liu, S., Maljovec, D., Wang, B., Bremer, P.-T., & Pascucci, V. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2640960 · Cited by 427
Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach
Lamy, J.-B., Sekar, B., Guezennec, G., Bouaud, J., & Séroussi, B. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.01.001 · Cited by 282
Metadata
- Authors
- Bertini, E., Tatu, A., & Keim, D.
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2011-12
- Volume
- 17
- Issue
- 12
- Pages
- 2203-2212
- ISSN
- 1077-2626 (ISSN-L)
- OpenAlex
- W2157379557
- Cited by
- 257
- References
- 55
- FWCI
- 16.22
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bertini, E., Tatu, A., & Keim, D. (2011). Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/tvcg.2011.229
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