This paper presents an efficient visualization and exploration approach for modeling and characterizing the relationships and uncertainties in the context of a multidimensional ensemble dataset. Its core is a novel dissimilarity-preserving projection technique that characterizes not only the relationships among the mean values of the ensemble data objects but also the relationships among the distributions of ensemble members. This uncertainty-aware projection scheme leads to an improved understanding of the intrinsic structure in an ensemble dataset. The analysis of the ensemble dataset is further augmented by a suite of visual encoding and exploration tools. Experimental results on both artificial and real-world datasets demonstrate the effectiveness of our approach.

Illustrating the differences between DTI fiber models is important for the purposes of group comparison, atlas construction, and uncertainty analysis. Standard approaches show difference between DTI models in 3D space, with either voxel-based or fiber based comparison. We proposes a new method that embeds a high-dimensional 3D fiber model as a continuous map in a 2D normalized space, built on fibers from all targeted fiber models. One advantage over previous 2D embedding approaches is that our method represents all fiber models in a unified coordinate system by taking a cluster-and-project routine, ensuring that closely located fibers from different models will be projected to close points on the 2D map. Using this projected 2D map, subtle differences that are difficult to distinguish in 3D space can be clearly displayed and individual fiber model can be effectively summarized. We applied our method to group comparison and analysis of various fiber models.

Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach.