title: Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration.
abstract: 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.
authors.forename: Haidong
authors.initials: H
authors.lastname: Chen
authors.forename: Song
authors.initials: S
authors.lastname: Zhang
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.forename: Honghui
authors.initials: H
authors.lastname: Mei
authors.forename: Jiawei
authors.initials: J
authors.lastname: Zhang
authors.forename: Andrew
authors.initials: A
authors.lastname: Mercer
authors.forename: Ronghua
authors.initials: R
authors.lastname: Liang
authors.forename: Huamin
authors.initials: H
authors.lastname: Qu