 |
| GI'98 Online Papers |
On the Use of Perceptual Cues and Data Mining for Effective Visualization of Scientific Datasets
Abstract
Scientific datasets are often difficult to analyse or visualize, due to
their large size and high dimensionality. We propose a two-step approach
to address this problem. We begin by using data mining algorithms to
identify areas of interest within the dataset. This allows us to
reduce a dataset's size and dimensionality, and to estimate missing
values or correct erroneous entries. We display the results of the
data mining step using visualization techniques based on perceptual cues.
Our visualization tools are designed to exploit the power of the low-level
human visual system. The result is a set of displays that allow users to
perform rapid and accurate exploratory data analysis.
In order to demonstrate our techniques, we visualized an environmental
dataset being used to model salmon growth and migration patterns. Data
mining was used to identify significant attributes and to provide accurate
estimates of plankton density. We used colour and texture to
visualize the significant attributes and estimated plankton densities for
each month for the years 1956 to 1964. Experiments run in our laboratory
showed that the colours and textures we chose support rapid and
accurate element identification, boundary detection, region tracking, and
estimation. The result is a visualization tool that allows users to
quickly locate specific plankton densities and the boundaries they form.
Users can compare plankton densities to other environmental conditions
like sea surface temperature and current strength. Finally, users can
track changes in any of the dataset's attributes on a monthly or yearly
basis.
The Paper
Compressed Postscript file
(389 Kb)
Full Paper (HTML Version)
Up to Graphics Interface home page