Glyphboard: Visual Exploration of High-Dimensional Data Combining Glyphs with Dimensionality Reduction

Kammer, Dietrich; Keck, Mandy; Gründer, Thomas; Maasch, Alexander; Thom, Thomas; Kleinsteuber, Martin; Groh, Rainer

doi:10.1109/tvcg.2020.2969060

Cited by 28 publications

(12 citation statements)

References 33 publications

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“…Researchers have proposed many strategies to improve pattern significance in data projections. Many works design new layouts or glyphs to generate visual summaries of adjacent points [30,40,67]. However, the projection may not contain significant clusters, making determining grouping boundaries difficult.…”

Section: Embedding-based Data Explorationmentioning

confidence: 99%

Incorporation of Human Knowledge Into Data Embeddings to Improve Pattern Significance and Interpretability

Zhou

2022

IEEE Trans. Visual. Comput. Graphics

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2 a1 b1 c1 b2 a2 c1 c2 Fig. 1. The visualization system interface that consists of three components: (a) Knowledge Editor (KE), (b) Data Projector (DP), and (c) Pattern Explainer (PE). The analyst is analyzing the Covid-19 dataset [2] that consists of 173 sequences, each recording the number of monthly-confirmed cases (per million people) from May 2020 to April 2021 of a country. The analyst creates six country groups (see colored tree nodes) according to their knowledge using (a) and incorporates the class information into the embeddings of countries to generate the projection with clear and class-consistent visual structures that foreshadow underlying data patterns. (b2)The embedding projection without knowledge where does not exist visual structure.

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Section: Embedding-based Data Explorationmentioning

confidence: 99%

Incorporation of Human Knowledge Into Data Embeddings to Improve Pattern Significance and Interpretability

Zhou

2022

IEEE Trans. Visual. Comput. Graphics

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“…Due to its computational efficiency, our approach can also be used to support interactive lenses such as the one proposed in Glyphboard (Kammer et al 2020). In Glyphboard, DR projections are plotted as normal scatterplots and as the user zooms in, the dots are replaced with circular glyphs.…”

Section: Lens Viewmentioning

confidence: 99%

Hagrid: using Hilbert and Gosper curves to gridify scatterplots

Cutura

Morariu

Cheng

et al. 2022

J Vis

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A common enhancement of scatterplots represents points as small multiples, glyphs, or thumbnail images. As this encoding often results in overlaps, a general strategy is to alter the position of the data points, for instance, to a grid-like structure. Previous approaches rely on solving expensive optimization problems or on dividing the space that alter the global structure of the scatterplot. To find a good balance between efficiency and neighborhood and layout preservation, we propose Hagrid, a technique that uses space-filling curves (SFCs) to “gridify” a scatterplot without employing expensive collision detection and handling mechanisms. Using SFCs ensures that the points are plotted close to their original position, retaining approximately the same global structure. The resulting scatterplot is mapped onto a rectangular or hexagonal grid, using Hilbert and Gosper curves. We discuss and evaluate the theoretic runtime of our approach and quantitatively compare our approach to three state-of-the-art gridifying approaches, DGrid, Small multiples with gaps SMWG, and CorrelatedMultiples CMDS, in an evaluation comprising 339 scatterplots. Here, we compute several quality measures for neighborhood preservation together with an analysis of the actual runtimes. The main results show that, compared to the best other technique, Hagrid is faster by a factor of four, while achieving similar or even better quality of the gridified layout. Due to its computational efficiency, our approach also allows novel applications of gridifying approaches in interactive settings, such as removing local overlap upon hovering over a scatterplot. Graphical abstract

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“…For example, a physician performs a categorization task when they diagnose a patient as sick or healthy based on their symptoms as features in a medical diagnosis [29]. In machine learning, categorization tasks can be helpful to categorize data into clusters based on their dimensions [20]. Data glyphs are a particularly appropriate and effective method to visually communicate categorization data because categorization tasks require the synthesis of these many dimensions of data to determine its category.…”

Section: Multi-colored Star Charts Chernoff Faces On a Cartogrammentioning

confidence: 99%