Dual View: Multivariate Visualization Using Linked Layouts of Objects and Dimensions


The display of multivariate data is a common task in data visualization. However as dimensionality increases, it becomes increasingly difficult to visualize all dimensions using standard multivariate visualization techniques, such as parallel coordinates. Dimension reduction is often used to show relationships between data objects in a lower-dimensional representation, but the relationships between data objects and the original dimensions is typically lost. We introduce Dual View, a visualization technique for high-dimensional datasets that directly represents both data objects and data dimensions in separate 2D layouts. Linked views, spatial aggregation, and iterative layout refinement enables the exploration of high-dimensional datasets. We present the underlying algorithms for layout and interaction, a prototype Dual View user interface, and some examples applying Dual View to multidimensional datasets.

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