Name
Abstract | ||
|---|---|---|
This paper presents an efficient visualization and exploration approach for modeling and characterizing the relationships and uncertainties in the context of a multidimensional ensemble dataset. Its core is a novel dissimilarity-preserving projection technique that characterizes not only the relationships among the mean values of the ensemble data objects but also the relationships among the distributions of ensemble members. This uncertainty-aware projection scheme leads to an improved understanding of the intrinsic structure in an ensemble dataset. The analysis of the ensemble dataset is further augmented by a suite of visual encoding and exploration tools. Experimental results on both artificial and real-world datasets demonstrate the effectiveness of our approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TVCG.2015.2410278 | IEEE Transactions on Visualization and Computer Graphics |
Keywords | Field | DocType |
Ensemble visualization, uncertainty quantification, uncertainty visualization, multidimensional data visualization | Data mining,Data visualization,Uncertainty quantification,Suite,Visualization,Computer science,Symmetric matrix,Solid modeling,Artificial intelligence,Ensemble learning,Machine learning,Encoding (memory) | Journal |
Volume | Issue | ISSN |
PP | 99 | 1077-2626 |
Citations | PageRank | References |
23 | 0.68 | 37 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haidong Chen | 1 | 64 | 3.69 |
| Song Zhang | 2 | 642 | 53.89 |
| Wei Chen | 3 | 1193 | 92.00 |
| Honghui Mei | 4 | 117 | 5.90 |
| Jiawei Zhang | 5 | 26 | 1.50 |
| Andrew E. Mercer | 6 | 125 | 5.82 |
| Ronghua Liang | 7 | 376 | 42.60 |
| Huamin Qu | 8 | 2033 | 115.33 |