Combining parallel coordinates with the reorderable matrix

There is evidence that the highlighting of the same data items in different views will improve the proficiency of knowledge acquisition tasks. Making the connection for the user will reduce the cognitive load of switching from one view to another and allows making observations that would not be possible from the two separate views. However, making the connection between conceptually different views might not be so straightforward if the view types are complex and different enough. We compare two conceptually different information visualization techniques called parallel coordinate plots and the reorderable matrix, and consider how the two representations could be combined. We report an experiment where the participants performed tasks with an application having a reorderable matrix and parallel coordinate views to the same data, with linking and without linking. It was found out that although the view linking initially slows the task performance, it will accelerate learning and it is well received by users.