Bing Wang

Bing Wang, Klaus Mueller

We test the hypothesis whether transforming a data matrix into a 3D shaded surface or even a volumetric display can be more appealing to humans than a scatterplot since it makes direct use of the innate 3D scene understanding capabilities of the human visual system. We also test whether 3D shaded displays can add a significant amount of information to the visualization of high-dimensional data, especially when enhanced with proper tools to navigate the various 3D subspaces. Our experiments suggest that mainstream users prefer shaded displays over scatterplots for visual cluster analysis tasks after receiving training for both. Our experiments also provide evidence that 3D displays can better communicate spatial relationships, size, and shape of clusters.

Bing Wang, Klaus Mueller

Analyzing high-dimensional data and finding hidden patterns is a difficult problem and has attracted numerous research efforts. Automated methods can be useful to some extent but bringing the data analyst into the loop via interactive visual tools can help the discovery process tremendously. An inherent problem in this effort is that humans lack the mental capacity to truly understand spaces exceeding three spatial dimensions. To keep within this limitation, we describe a framework that decomposes a high-dimensional data space into a continuum of generalized 3D subspaces. Analysts can then explore these 3D subspaces individually via the familiar trackball interface, but using additional facilities to smoothly transition to adjacent subspaces for expanded space comprehension. Since the number of such subspaces suffers from combinatorial explosion, we provide a set of data-driven subspace selection and navigation tools which can guide users to interesting subspaces and views. A subspace trail map allows users to manage the explored subspaces, and also helps them navigate within and across any higher-dimensional subspaces identified by clustering. Both trackball and trail map are each embedded into a word cloud of attribute labels, sized according to the relevance of the associated data dimensions in the currently selected subspace. Finally, a view gallery helps users keep their bearings and return to interesting subspaces and views. We demonstrate our system via several use cases in a diverse set of application areas, such as cluster analysis and refinement, information discovery, and supervised training of classifiers.

Bing Wang, Puripant Ruchikachorn, Klaus Mueller

High-dimensional data visualization has been attracting much attention. To fully test related software and algorithms, researchers require a diverse pool of data with known and desired features. Test data do not always provide this, or only partially. Here we propose the paradigm WYDIWYGS (What You Draw Is What You Get). Its embodiment, Sketch Pad ND, is a tool that allows users to generate high-dimensional data in the same interface they also use for visualization. This provides for an immersive and direct data generation activity, and furthermore it also enables users to interactively edit and clean existing high-dimensional data from possible artifacts. Sketch Pad ND offers two visualization paradigms, one based on parallel coordinates and the other based on a relatively new framework using an N-D polygon to navigate in high-dimensional space. The first interface allows users to draw arbitrary profiles of probability density functions along each dimension axis and sketch shapes for data density and connections between adjacent dimensions. The second interface embraces the idea of sculpting. Users can carve data at arbitrary orientations and refine them wherever necessary. This guarantees the data generated is truly high-dimensional. We demonstrate our tool's usefulness in real data visualization scenarios.