Interactive Hierarchical Displays: A General Framework for
Visualization and Exploration of Large Multivariate Data Sets
Authors: Jing Yang, Matthew O. Ward, and Elke A. Rundensteiner
Numerous multivariate visualization techniques and systems
have been developed in the past three decades to visually analyze
and explore multivariate data being produced daily in application
areas ranging from stock markets to the earth and space spaces.
However, traditional multivariate visualization techniques typically
do not scale well to large multivariate data sets, with the latter
becoming more and more common nowadays. This paper proposes a
general framework for interactive hierarchical displays (IHDs)
to tackle the clutter problem faced by traditional multivariate
visualization techniques when analyzing large data sets. The underlying
principle of this framework is to develop a multi-resolution view
of the data via hierarchical clustering, and to use hierarchical
variations of traditional multivariate visualization techniques
to convey aggregation information about the resulting clusters.
Users can then explore their desired focus region at dierent
levels of detail, using our suite of navigation and ltering tools.
We describe this IHD framework and its full implementation on
four traditional multivariate visualization techniques, namely,
parallel coordinates [9,19], star glyphs , scatterplot matrices
, and dimensional stacking , as implemented in the XmdvTool
system [18,13,6,7]. We also describe an empirical evaluation that
veried the eectiveness of the interactive hierarchical displays.
Key words: Large-scale multivariate data visualization, exploratory
data analysis, hierarchical data exploration
Graphics Journal, 2002.