Hierarchical Exploration of Large Multivariate Data Sets
Authors: Jing Yang, Matthew O. Ward and Elke A. Rundensteiner
Multivariate data visualization techniques are often limited
in terms of the number of data records that can be simultaneously
displayed in a manner that allows ready interpretation. Due to
the size of the screen and number of pixels available, visualizing
more than a few thousand data points generally leads to clutter
and occlusion. This in turn restricts our ability to detect, classify,
and measure phenomena of interest, such as clusters, anomalies,
trends, and patterns. In this paper we describe our experiences
in the development of multi-resolution visualization techniques
for large multivariate data sets. By hierarchically clustering
the data and displaying aggregation information for each cluster,
we can examine the data set at multiple levels of abstraction.
In addition, by providing powerful navigation and ltering operations,
we can create an environment suitable for interactive exploration
without overloading the user with dense information displays.
In this paper, we illustrate that our hierarchical displays are
general by successfully applying them to four popular yet non-scalable
visualizations, namely parallel coordinates, glyphs, scatterplot
matrices and dimensional stacking.
Seminar on Scientific Visualization 2000.