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# Flexible Subset Selection for Data Visualization

## Abstract
Subset selection is crucial in data visualization for decluttering, summarizing, and emphasizing key insights. This project proposes a unified approach using multi-criterion optimization for flexible subset selection strategies. By defining objective functions, we tailor subsets to meet specific visualization needs, leveraging both traditional and innovative selection methods. General-purpose solvers facilitate rapid prototyping, demonstrated through realistic examples of scatterplot decluttering, dataset summarization, and exemplar highlighting.
Subset selection has many uses in data visualization, but each use often has a specialized strategy or implementation.
We propose a general strategy for organizing, designing, and prototyping subset selection for visualization by casting the problem in terms of multi-criterion optimization.
This strategy allows us to select subsets for typical uses in data visualization.
We can incorporate both standard and novel selection approaches using this strategy.
It also provides several advantages.
Objective functions encode criteria, representing the desired properties of a subset.
The objectives can be selected, blended and tuned to meet the needs of particular visualizations.
General purpose solvers can be used as an effective prototyping strategy.
We applied the proposed strategy to example situations of designing visualizations using realistic example datasets, objectives, and charts.
These demonstrate the effectiveness and flexibility of the strategy in common visualization uses such as decluttering scatterplots, summarizing datasets, or highlighting exemplars.

# Flexible Subset Selection Python Package

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