A framework for uncertainty-aware visual analytics
Visual analytics has become an important tool for gaining insight on large and complex collections of data. Numerous statistical tools and data transformations, such as projections, binning and clustering, have been coupled with visualization to help analysts understand data better and faster. Howev...
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| Veröffentlicht in: | 2009 IEEE Symposium on Visual Analytics Science and Technology S. 51 - 58 |
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| Format: | Tagungsbericht |
| Sprache: | Englisch |
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IEEE
01.10.2009
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| ISBN: | 9781424452835, 142445283X |
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| Abstract | Visual analytics has become an important tool for gaining insight on large and complex collections of data. Numerous statistical tools and data transformations, such as projections, binning and clustering, have been coupled with visualization to help analysts understand data better and faster. However, data is inherently uncertain, due to error, noise or unreliable sources. When making decisions based on uncertain data, it is important to quantify and present to the analyst both the aggregated uncertainty of the results and the impact of the sources of that uncertainty. In this paper, we present a new framework to support uncertainty in the visual analytics process, through statistic methods such as uncertainty modeling, propagation and aggregation. We show that data transformations, such as regression, principal component analysis and k-means clustering, can be adapted to account for uncertainty. This framework leads to better visualizations that improve the decision-making process and help analysts gain insight on the analytic process itself. |
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| AbstractList | Visual analytics has become an important tool for gaining insight on large and complex collections of data. Numerous statistical tools and data transformations, such as projections, binning and clustering, have been coupled with visualization to help analysts understand data better and faster. However, data is inherently uncertain, due to error, noise or unreliable sources. When making decisions based on uncertain data, it is important to quantify and present to the analyst both the aggregated uncertainty of the results and the impact of the sources of that uncertainty. In this paper, we present a new framework to support uncertainty in the visual analytics process, through statistic methods such as uncertainty modeling, propagation and aggregation. We show that data transformations, such as regression, principal component analysis and k-means clustering, can be adapted to account for uncertainty. This framework leads to better visualizations that improve the decision-making process and help analysts gain insight on the analytic process itself. |
| Author | Yu-Hsuan Chan Correa, C.D. Kwan-Liu Ma |
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| SubjectTerms | Blogs Data analysis Data Transformations Data visualization Decision making Humans Model Fitting Principal component analysis Sampling methods Statistical analysis Uncertainty Visual analytics |
| Title | A framework for uncertainty-aware visual analytics |
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