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A Recursive Subdivision Technique for Sampling Multi-class Scatterplots

An interactive demo application for the algorithm proposed in our IEEE VIS 2019 technical paper. More details of our project can be found here.

The program also provides some interactive functions:

  1. select interested classes in the bottom left panel;
  2. drag and select a local area, then you can select new grid size for this area;
  3. the information of the local area you selected will be shown in the right panel.

Screenshot of the application.

Results

Input scatterplots and their results.

Disclaimer

This is a reimplemented demo with focus on interactivity, and not the code that was used to generate the images and timings in the paper.

Abstract

We present a non-uniform recursive sampling technique for multi-class scatterplots, with the specific goal of faithfully presenting relative data and class densities, while preserving major outliers in the plots. Our technique is based on a customized binary kd-tree, in which leaf nodes are created by recursively subdividing the underlying multi-class density map. By backtracking, we merge leaf nodes until they encompass points of all classes for our subsequently applied outlier-aware multi-class sampling strategy. A quantitative evaluation shows that our approach can better preserve outliers and at the same time relative densities in multi-class scatterplots compared to the previous approaches, several case studies demonstrate the effectiveness of our approach in exploring complex and real world data.

Citation

@ARTICLE{sampling2019,
author={X. {Chen} and T. {Ge} and J. {Zhang} and B. {Chen} and C. {Fu} and O. {Deussen} and Y. {Wang}},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={A Recursive Subdivision Technique for Sampling Multi-class Scatterplots},
year={2020},
volume={26},
number={1},
pages={729-738},
keywords={Visualization;Data visualization;Measurement;Sampling methods;Estimation;Clutter;Image color analysis;Scatterplot;multi-class sampling;kd-tree;outlier;relative density},
doi={10.1109/TVCG.2019.2934541},
ISSN={2160-9306},
month={Jan}
}

Dependencies

The following libraries are required:

  • Qt5Core
  • Qt5GUI
  • Qt5Widgets
  • Qt5Svg
  • Qt5PrintSupport