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- W2989664530 abstract "Visual representation of large datasets should allow us to focus on essential dimensions when restricted to limited visual space. This paper presents an approach for abstracting multi-dimensional data with a focus on grouping the individual attributes based on visual features (or Scagnostics) such as density, skewness, shape, outliers, and texture. Working directly with these visual characterizations, we propose a prototype, called FSelector, to guide users when interactively exploring high dimensional datasets. In particular, selected (leading) variables are organized in a grid layout, allowing users to rapidly identify interesting pairs of variables and to focus on analyzing the original variables directly." @default.
- W2989664530 created "2019-12-05" @default.
- W2989664530 creator A5032280607 @default.
- W2989664530 date "2019-06-03" @default.
- W2989664530 modified "2023-09-26" @default.
- W2989664530 title "FSelector: Variable Selection Using Visual Features" @default.
- W2989664530 doi "https://doi.org/10.20380/gi2019.07" @default.
- W2989664530 hasPublicationYear "2019" @default.
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