This project is part of the Collaborative Research Center SFB-TRR 161 "Quantitative Methods for Visual Computing"
High-dimensional data analysis requires dealing with numerous challenges, such as selecting meaningful dimensions, finding relevant projections, and removing noise. As a result, the extraction of relevant and meaningful information from high-dimensional data is a difficult problem. This project aims at advancing the field of quality-metric-driven data visualization with the central research question of how to quantify the quality of transformations and mappings of high-dimensional data for visual analytics.
- How can we measure and quantify the quality of a visualization? In which way do methods in the data space differ from methods in the image space?
- How can we compare the measured quality of a visualization with the perception of a human?
- How can the user be involved into a quality-metric-driven process of visual mappings and transformations?
- What is the influence of perceptual effects on quality measures?
- Can we enhance the visual representation of information by introducing perceptual effects into visualizations?
SMARTexplore: A novel, table-based Visual Analytics approach to identify and understand clusters, correlations, and complex patterns in high-dimensional data. We use quality metrics for a semi-automatic reliability analysis and a pattern-based layout of rows and columns. Pattern matching and subspace analysis algorithms are used to reveal interesting findings. Try it out here.
This research project received funding from the DFG (Deutsche Forschungsgemeinschaft) within the SFB-TRR 161.