Structural Decomposition Trees: Semantic and Practical Implications

The visualization of high-dimensional data is a challenging research topic. Existing approaches can usually be assigned to either relation or value visualizations. Merging approaches from both classes into a single integrated strategy, Structural Decomposition Trees (SDTs) represent a completely novel visualization approach for high-dimensional data. Although this method is new and promising, statements on how to use and apply the technique in the context of real-world applications are still missing. This paper discusses how SDTs can be interpreted and interacted with to gain insights about the data more effectively. First, it is discussed what properties about the data can be obtained by an interpretation of the initial projection. These statements are also valid for other projections based on principal components analysis, addressing a frequent problem when applying this technique. Further, a detailed and task-oriented interaction guideline shows how provided interaction methods can be utilized effectively for data exploration. The results obtained by an application of these guidelines in air quality research indicate that much insight can be gained even for large and complex data sets. This justifies and further motivates the usefulness and wide applicability of SDTs as a novel visualization approach for high-dimensional data.