Visual Analysis of Large Multivariate Scattered Data using Clustering and Probabilistic Summaries.

Area of Science:

• Data Visualization
• Scientific Computing
• High-Dimensional Data Analysis

Background:

• Scientific simulations generate massive datasets, overwhelming current interactive visualization and analysis tools.
• Existing methods struggle with the scale and complexity of multivariate, arbitrarily structured data.

Purpose of the Study:

• To develop a compact probabilistic representation for interactively visualizing and analyzing large, scattered, multivariate datasets.
• To enable efficient storage and retrieval of high-dimensional data distributions within clusters.

Main Methods:

• Modeling clusters of arbitrarily structured multivariate data using probability distributions.
• Representing high-dimensional distributions via combinations of low-dimensional Gaussian mixture models.
• Applying interactive visual analysis techniques, including density plots, parallel coordinates, and spatial splatting of anisotropic Gaussians.

Main Results:

• Demonstrated efficient representation and storage of high-dimensional distributions.
• Successfully applied interactive techniques like density plots and parallel coordinates to the probabilistic representation.
• Evaluated the approach on large, real-world datasets, showing significant scalability improvements.

Conclusions:

• The proposed compact probabilistic representation effectively handles large, scattered, multivariate datasets for interactive visualization and analysis.
• The method offers a scalable solution to the challenges posed by rapidly growing scientific simulation data sizes.
• Future work can explore further optimizations and applications in diverse scientific domains.