From 4a9b02e871d7be3f4ed00d4c4792affc27a97597 Mon Sep 17 00:00:00 2001 From: Connor Bailey Date: Mon, 5 Aug 2024 16:28:00 -0600 Subject: [PATCH] Update README.md --- README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 29c9854..638b09b 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,16 @@ # 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