Visualization of high-dimensional point clouds using their density distribution's topology
We present a novel method to visualize multidimensional point clouds. While conventional visualization techniques, like scatterplot matrices or parallel coordinates, have issues with either overplotting of entities or handling many dimensions, we abstract the data using topological methods before pr...
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| Main Authors: | , , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
04 February 2011
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| In: |
IEEE transactions on visualization and computer graphics
Year: 2011, Volume: 17, Issue: 11, Pages: 1547-1559 |
| ISSN: | 1941-0506 |
| DOI: | 10.1109/TVCG.2011.27 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/TVCG.2011.27
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| Author Notes: | Patrick Oesterling, Christian Heine, Heike Janicke, Gerik Scheuermann, Gerhard Heyer |
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| 245 | 1 | 0 | |a Visualization of high-dimensional point clouds using their density distribution's topology |c Patrick Oesterling, Christian Heine, Heike Janicke, Gerik Scheuermann, Gerhard Heyer |
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| 520 | |a We present a novel method to visualize multidimensional point clouds. While conventional visualization techniques, like scatterplot matrices or parallel coordinates, have issues with either overplotting of entities or handling many dimensions, we abstract the data using topological methods before presenting it. We assume the input points to be samples of a random variable with a high-dimensional probability distribution which we approximate using kernel density estimates on a suitably reconstructed mesh. From the resulting scalar field we extract the join tree and present it as a topological landscape, a visualization metaphor that utilizes the human capability of understanding natural terrains. In this landscape, dense clusters of points show up as hills. The nesting of hills indicates the nesting of clusters. We augment the landscape with the data points to allow selection and inspection of single points and point sets. We also present optimizations to make our algorithm applicable to large data sets and to allow interactive adaption of our visualization to the kernel window width used in the density estimation. | ||
| 650 | 4 | |a Approximation methods | |
| 650 | 4 | |a Clustering | |
| 650 | 4 | |a Data visualization | |
| 650 | 4 | |a Density functional theory | |
| 650 | 4 | |a graphs | |
| 650 | 4 | |a Kernel | |
| 650 | 4 | |a pattern analysis | |
| 650 | 4 | |a Piecewise linear approximation | |
| 650 | 4 | |a point clouds | |
| 650 | 4 | |a Runtime | |
| 650 | 4 | |a Topology | |
| 650 | 4 | |a topology. | |
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| 700 | 1 | |a Scheuermann, Gerik |e VerfasserIn |4 aut | |
| 700 | 1 | |a Heyer, Gerhard |e VerfasserIn |4 aut | |
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