Name
Abstract | ||
|---|---|---|
Discovering and extracting linear trends and correlations in datasets is very important for analysts to understand multivariate phenom- ena. However, current widely used multivariate visualization tech- niques, such as parallel coordinates and scatterplot matrices, fail to reveal and illustrate such linear relationships intuitively, especially when more than 3 variables are involved or multiple trends coex- ist in the dataset. We present a novel multivariate model parameter space visualization system that helps analysts discover single and multiple linear patterns and extract subsets of data that fit a model well. Using this system, analysts are able to explore and navigate in model parameter space, interactively select and tune patterns, and refine the model for accuracy using computational techniques. We build connections between model space and data space visu- ally, allowing analysts to employ their domain knowledge during exploration to better interpret the patterns they discover and their validity. Case studies with real datasets are used to investigate the effectiveness of the visualizations. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/VAST.2009.5333431 | IEEE VAST |
Keywords | Field | DocType |
knowledge discovery,multivariate linear model construction,model space visualization. index terms: h.5.2 information interfaces and presentation: user interfaces—graphical user interfaces,visual analysis,graphic user interface,domain knowledge,computational modeling,data visualisation,data mining,indexing terms,color,parallel coordinates,navigation,visual system,data models,parameter space,data visualization,user interface | Data modeling,Data mining,Data visualization,Domain knowledge,Computer science,Multivariate statistics,Visualization,Parallel coordinates,Knowledge extraction,Artificial intelligence,Parameter space,Machine learning | Conference |
Citations | PageRank | References |
18 | 0.74 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyu Guo | 1 | 512 | 39.61 |
| Matthew O. Ward | 2 | 1757 | 189.48 |
| Elke A. Rundensteiner | 3 | 4076 | 700.65 |