Browsing by Author "Heinrich, Julian"
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Item Open Access Visualization techniques for parallel coordinates(2013) Heinrich, Julian; Weiskopf, Daniel (Prof. Dr.)Visualization plays a key role in knowledge discovery, visual data exploration, and visual analytics. Static images are an effective tool for visual communication, summarization, and pattern extraction in large and complex datasets. Only together with human-computer-interaction techniques, visual interfaces enable an analyst to explore large information spaces and to drive the whole analytical reasoning process. Scatterplots and parallel coordinates are well-recognized visualization techniques that are commonly employed for statistics (both explorative and descriptive) and data-mining, but are also gaining importance for scientific visualization. While scatterplots are restricted to the display of at most three dimensions due to the orthogonal layout of coordinate axes, a parallel arrangement allows for the visualization of multiple attributes of a dataset. Although both techniques rely on projections of higher-dimensional geometry and are related by a point–line duality, parallel coordinates enjoy great popularity for the visualization and analysis of multivariate data. Despite their popularity, parallel coordinates are subject to a number of limitations that remain to be solved. For large datasets, the potentially high amount of overlapping lines may hinder the observer from visually extracting meaningful patterns. Encoding observations with polylines make it difficult to follow lines over all dimensions, as they lose visual continuation across the axes. Clusters cannot be represented by the geometry of lines, and the order of axes has a high impact on the patterns exhibited by parallel coordinates. This thesis presents visualization techniques for parallel coordinates that address these limitations. As a foundation, an extensive review of the state of the art of parallel coordinates will be given. Based on the point–line duality, the existing model of continuous scatterplots is adapted to parallel coordinates for the visualization of data defined on continuous domains. To speed up computation and obtain interactive frame rates, a scalable and progressive rendering algorithm is introduced that further allows for arbitrary reconstruction and interpolation schemes. A curve-bundling model for parallel coordinates is evaluated with a user study showing that bundling is effective for cluster visualization based on geometric cues while being equally capable of revealing correlations between neighboring axes. To address the axis-order problem, a graph-based approach is presented that allows for the visualization of all pairwise relations in a matrix layout without redundancy. Finally, the use of parallel coordinates is demonstrated for real datasets from computational fluid dynamics, motion capturing, bioinformatics, and systems biology.