Misssing Data in Visualization

Problem

Data is never perfect and often plagued by quality issues. A very common of these is missing data. However, visualization approaches, such as scatterplots, often expect complete datasets. So do algorithmic approaches, such as clustering or dimension reduction. Remedies to the problem, such as imputation of missing data, may create other kinds of uncertainties. 

Aim

SE: Collect and present scientific literature that covers how static and interactive visualizations deal with missing data.

Other information

References:

• S. J. Fernstad and J. J. Westberg, “To Explore What Isn’t There—Glyph-Based Visualization for Analysis of Missing Values,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 10, pp. 3513–3529, Oct. 2022, doi: 10.1109/TVCG.2021.3065124.
• H. Song and D. A. Szafir, “Where’s My Data? Evaluating Visualizations with Missing Data,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 914–924, Jan. 2019, doi: 10.1109/TVCG.2018.2864914.

Contact

• Nikolaus Piccolotto