Interactive High-Dimensional Data Visualization

TLDR

The paper builds on prior work on focusing and linking in data visualization, emphasizing high‑dimensional projections within XGobi and noting that its sections, especially case studies, can be read independently. The authors aim to present a taxonomy of interactive data visualization based on finding Gestalt, posing queries, and making comparisons, and to introduce the multivariate system XGobi. They describe how focusing, linking, and arranging view manipulations—implemented via high‑dimensional projections, linked scatterplot brushing,.

Abstract

Abstract We propose a rudimentary taxonomy of interactive data visualization based on a triad of data analytic tasks: finding Gestalt, posing queries, and making comparisons. These tasks are supported by three classes of interactive view manipulations: focusing, linking, and arranging views. This discussion extends earlier work on the principles of focusing and linking and sets them on a firmer base. Next, we give a high-level introduction to a particular system for multivariate data visualization—XGobi. This introduction is not comprehensive but emphasizes XGobi tools that are examples of focusing, linking, and arranging views; namely, high-dimensional projections, linked scatterplot brushing, and matrices of conditional plots. Finally, in a series of case studies in data visualization, we show the powers and limitations of particular focusing, linking, and arranging tools. The discussion is dominated by high-dimensional projections that form an extremely well-developed part of XGobi. Of particular interest are the illustration of asymptotic normality of high-dimensional projections (a theorem of Diaconis and Freedman), the use of high-dimensional cubes for visualizing factorial experiments, and a method for interactively generating matrices of conditional plots with high-dimensional projections. Although there is a unifying theme to this article, each section—in particular the case studies—can be read separately.