BibTex
@inproceedings{Dang:2019:10.20380/GI2019.07,
author = {Dang, Tommy},
title = {FSelector: Variable Selection Using Visual Features},
booktitle = {Proceedings of Graphics Interface 2019},
series = {GI 2019},
year = {2019},
issn = {0713-5424},
isbn = {978-0-9947868-4-5},
location = {Kingston, Ontario},
numpages = {9},
doi = {10.20380/GI2019.07},
publisher = {Canadian Information Processing Society},
}
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.