19,548 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
TS2PACK: A Two-Level Tabu Search for the Three-dimensional Bin Packing Problem
Three-dimensional orthogonal bin packing is a problem NP-hard in the strong sense where a set of boxes must be orthogonally packed into the minimum number of three-dimensional bins. We present a two-level tabu search for this problem. The first-level aims to reduce the number of bins. The second optimizes the packing of the bins. This latter procedure is based on the Interval Graph representation of the packing, proposed by Fekete and Schepers, which reduces the size of the search space. We also introduce a general method to increase the size of the associated neighborhoods, and thus the quality of the search, without increasing the overall complexity of the algorithm. Extensive computational results on benchmark problem instances show the effectiveness of the proposed approach, obtaining better results compared to the existing one
- …