8,115 research outputs found
Overlap Removal of Dimensionality Reduction Scatterplot Layouts
Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous
visualization tool for analyzing multidimensional data items with presence in
different areas. Despite its popularity, scatterplots suffer from occlusion,
especially when markers convey information, making it troublesome for users to
estimate items' groups' sizes and, more importantly, potentially obfuscating
critical items for the analysis under execution. Different strategies have been
devised to address this issue, either producing overlap-free layouts, lacking
the powerful capabilities of contemporary DR techniques in uncover interesting
data patterns, or eliminating overlaps as a post-processing strategy. Despite
the good results of post-processing techniques, the best methods typically
expand or distort the scatterplot area, thus reducing markers' size (sometimes)
to unreadable dimensions, defeating the purpose of removing overlaps. This
paper presents a novel post-processing strategy to remove DR layouts' overlaps
that faithfully preserves the original layout's characteristics and markers'
sizes. We show that the proposed strategy surpasses the state-of-the-art in
overlap removal through an extensive comparative evaluation considering
multiple different metrics while it is 2 or 3 orders of magnitude faster for
large datasets.Comment: 11 pages and 9 figure
Stress-Minimizing Orthogonal Layout of Data Flow Diagrams with Ports
We present a fundamentally different approach to orthogonal layout of data
flow diagrams with ports. This is based on extending constrained stress
majorization to cater for ports and flow layout. Because we are minimizing
stress we are able to better display global structure, as measured by several
criteria such as stress, edge-length variance, and aspect ratio. Compared to
the layered approach, our layouts tend to exhibit symmetries, and eliminate
inter-layer whitespace, making the diagrams more compact
On Semantic Word Cloud Representation
We study the problem of computing semantic-preserving word clouds in which
semantically related words are close to each other. While several heuristic
approaches have been described in the literature, we formalize the underlying
geometric algorithm problem: Word Rectangle Adjacency Contact (WRAC). In this
model each word is associated with rectangle with fixed dimensions, and the
goal is to represent semantically related words by ensuring that the two
corresponding rectangles touch. We design and analyze efficient polynomial-time
algorithms for some variants of the WRAC problem, show that several general
variants are NP-hard, and describe a number of approximation algorithms.
Finally, we experimentally demonstrate that our theoretically-sound algorithms
outperform the early heuristics
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