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

• Type: Journal
• Year: 2016
• Where: IUI
• Authors: David Gotz, Shun Sun, Nan Cao
• Keywords: IUI 2016, Software Engineering