The Parallel Coordinates Matrix
| dc.contributor.author | Heinrich, Julian | en_US |
| dc.contributor.author | Stasko, John | en_US |
| dc.contributor.author | Weiskopf, Daniel | en_US |
| dc.contributor.editor | Miriah Meyer and Tino Weinkaufs | en_US |
| dc.date.accessioned | 2013-11-08T10:22:31Z | |
| dc.date.available | 2013-11-08T10:22:31Z | |
| dc.date.issued | 2012 | en_US |
| dc.description.abstract | We introduce the parallel coordinates matrix (PCM) as the counterpart to the scatterplot matrix (SPLOM). Using a graph-theoretic approach, we determine a list of axis orderings such that all pairwise relations can be displayed without redundancy while each parallel-coordinates plot can be used independently to visualize all variables of the dataset. Therefore, existing axis-ordering algorithms, rendering techniques, and interaction methods can easily be applied to the individual parallel-coordinates plots. We demonstrate the value of the PCM in two case studies and show how it can serve as an overview visualization for parallel coordinates. Finally, we apply existing focus-and-context techniques in an interactive setup to support a detailed analysis of multivariate data. | en_US |
| dc.description.seriesinformation | EuroVis - Short Papers | en_US |
| dc.identifier.isbn | 978-3-905673-91-3 | en_US |
| dc.identifier.uri | https://doi.org/10.2312/PE/EuroVisShort/EuroVisShort2012/037-041 | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Categories and Subject Descriptors (according to ACM CCS): Probability and Statistics [G.3]: Multivariate Statistics, Computer Graphics [I.3.3]: Picture/Image Generation-Display algorithms | en_US |
| dc.title | The Parallel Coordinates Matrix | en_US |