Two-dimensional scatterplot ordering

Users cannot, however, see the overview of entire relationships between variables at a glance in the ordered hst. Overviews are important because they can show the whole distribution and reveal interesting parts of data. We have implemented a new version of the score overview for 2D projections. It is an m-hy-m grid view where aU dimensions are aligned in the rows and colurrms. Each ceU of the score overview represents a scatterplot whose horizontal and vertical axes are dimensions 

While the ordered list shows the numerical score values of relationships between two dimensions, the interactive scatterplot browser best displays the relationship graphically. In the scatterplot browser, users can quickly take a look at scatterplots by using item sliders attached to the scatterplot view. Simply by dragging the vertical or horizontal item slider bar, users can change the dimension for the horizontal or vertical axis. With the current version implemented in HCE 3.0, users can investigate multiple scatterplots at the same time. They can select several scatterplots in the 

Again interesting ranking criteria might be different from user to user or from application to application. We have chosen the following six ranking criteria that we think are fundamental and common for scatterplots, and we have implemented them in HCE. The first three criteria are useful to reveal statistical (linear or quadratic) relationships between two dimensions (or variables), and the next three are useful to find scatterplots of interesting distributions. 

1 Correlation Coefficient (-1 to 1) For the first criterion, we use Pearson s correlation coefficient (r) for a scatterplot (5) with n points defined as 

3 Quadracity (0 to inf) If two variables show a strong linear relationship, they also produce a small error for curvilinear regression because the linear 

---