Interactive High-Dimensional Data Visualization

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.     

Manual Controls for High-Dimensional Data Projections

Abstract

Projections of high-dimensional data onto low-dimensional subspaces provide insightful views for understanding multivariate relationships. This article discusses how to manually control the variable contributions to the projection. The user has control of the way a particular variable contributes to the viewed projection and can interactively adjust the variable's contribution. These manual controls complement the automatic views provided by a grand tour, or a guided tour, and give greatly improved flexibility to data analysts.     

Orca: A Visualization Toolkit for High-Dimensional Data

Abstract

This article describes constructing interactive and dynamic linked data views using the Java programming language. The data views are designed for data that have a multivariate component. The approach to displaying data comes from earlier research on building statistical graphics based on data pipelines, in which different aspects of data processing and graphical rendering are organized conceptually into segments of a pipeline. The software design takes advantage of the object-oriented nature of the Java language to open up the data pipeline, allowing developers to have greater control over their visualization applications. Importantly, new types of data views coded to adhere to a few simple design requirements can easily be integrated with existing pipe sections. This allows access to sophisticated linking and dynamic interaction across all (new and existing) view types. Pipe segments can be accessed from data analysis packages such as Omegahat or R, providing a tight coupling of visual and numerical methods.