531,417 research outputs found
Conditional Parallel Coordinates
Parallel Coordinates are a popular data visualization technique for
multivariate data. Dating back to as early as 1880 PC are nearly as old as John
Snow's famous cholera outbreak map of 1855, which is frequently regarded as a
historic landmark for modern data visualization. Numerous extensions have been
proposed to address integrity, scalability and readability. We make a new case
to employ PC on conditional data, where additional dimensions are only unfolded
if certain criteria are met in an observation. Compared to standard PC which
operate on a flat set of dimensions the ontology of our input to Conditional
Parallel Coordinates is of hierarchical nature. We therefore briefly review
related work around hierarchical PC using aggregation or nesting techniques.
Our contribution is a visualization to seamlessly adapt PC for conditional data
under preservation of intuitive interaction patterns to select or highlight
polylines. We conclude with intuitions on how to operate CPC on two data sets:
an AutoML hyperparameter search log, and session results from a conversational
agent.Comment: 5 pages, 8 figures, VIS 2019 Short Paper
On the local structure of Lorentzian Einstein manifolds with parallel distribution of null lines
We study transformations of coordinates on a Lorentzian Einstein manifold
with a parallel distribution of null lines and show that the general Walker
coordinates can be simplified. In these coordinates, the full Lorentzian
Einstein equation is reduced to equations on a family of Einstein Riemannian
metrics.Comment: Dedicated to Dmitri Vladimirovich Alekseevsky on his 70th birthda
Angle-Uniform Parallel Coordinates
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.Comment: Computational Visual Media, 202
Speaking Stata: Graphing agreement and disagreement
Many statistical problems involve comparison and, in particular, the assessment of agreement or disagreement between data measured on identical scales. Some commonly used plots are often ineffective in assessing the fine structure of such data, especially scatterplots of highly correlated variables and plots of values measured "before" and "after" using tilted line segments. Valuable alternatives are available using horizontal reference patterns, changes plotted as parallel lines, and parallel coordinates plots. The quantities of interest (usually differences on some scale) should be shown as directly as possible, and the responses of given individuals should be identified as easily as possible. Copyright 2004 by StataCorp LP.graphics, comparison, agreement, paired data, panel data, scatterplot, difference-mean plot, Bland-Altman plot, parallel lines plot, parallel coordinates plot, pairplot, parplot, linkplot, Tukey
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