On the Use of Perceptual Cues & Data Mining for Effective Visualization of Scientific Datasets

Scientific datasets are often difficult to analyse or visualize, due to their large size and high dimensionality. We propose a twostep 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. CR Categories: H.5.2 [Information Interfaces and Presentation]: User Interfaces—ergonomics, screen design, theory and methods; I.3.6 [Computer Graphics]: Methodology and Techniques— ergonomics, interaction techniques; J.2 [Physical Sciences and Engineering]: Earth and Atmospheric Sciences