  We present angle-uniform parallel coordinates, a data-independent technique
that deforms the image plane of parallel coordinates so that the angles of
linear relationships between two variables are linearly mapped along the
horizontal axis of the parallel coordinates plot. Despite being a common method
for visualizing multidimensional data, parallel coordinates are ineffective for
revealing positive correlations since the associated parallel coordinates
points of such structures may be located at infinity in the image plane and the
asymmetric encoding of negative and positive correlations may lead to
unreliable estimations. To address this issue, we introduce a transformation
that bounds all points horizontally using an angle-uniform mapping and shrinks
them vertically in a structure-preserving fashion; polygonal lines become
smooth curves and a symmetric representation of data correlations is achieved.
We further propose a combined subsampling and density visualization approach to
reduce visual clutter caused by overdrawing. Our method enables accurate visual
pattern interpretation of data correlations, and its data-independent nature
makes it applicable to all multidimensional datasets. The usefulness of our
method is demonstrated using examples of synthetic and real-world datasets.
