Sciweavers

Share
IUI
2016
ACM

Adaptive Contextualization: Combating Bias During High-Dimensional Visualization and Data Selection

4 years 3 months ago
Adaptive Contextualization: Combating Bias During High-Dimensional Visualization and Data Selection
Large and high-dimensional real-world datasets are being gathered across a wide range of application disciplines to enable data-driven decision making. Interactive data visualization can play a critical role in allowing domain experts to select and analyze data from these large collections. However, there is a critical mismatch between the very large number of dimensions in complex real-world datasets and the much smaller number of dimensions that can be concurrently visualized using modern techniques. This gap in dimensionality can result in high levels of selection bias that go unnoticed by users. The bias can in turn threaten the very validity of any subsequent insights. In this paper, we present Adaptive Contextualization (AC), a novel approach to interactive visual data selection that is specifically designed to combat the in
David Gotz, Shun Sun, Nan Cao
Added 06 Apr 2016
Updated 06 Apr 2016
Type Journal
Year 2016
Where IUI
Authors David Gotz, Shun Sun, Nan Cao
Comments (0)
books