MultiLayerMatrix: Visualizing Large Taxonomic Datasets

dc.contributor.authorDang, Tuan Nhonen_US
dc.contributor.authorCui, Hongen_US
dc.contributor.authorForbes, Angus G.en_US
dc.contributor.editorNatalia Andrienko and Michael Sedlmairen_US
dc.date.accessioned2016-06-09T09:32:12Z
dc.date.available2016-06-09T09:32:12Z
dc.date.issued2016en_US
dc.description.abstractAdjacency matrices can be a useful way to visualize dense networks. However, they do not scale well as the network size increases due to limited screen space, especially when the number of rows and columns exceeds the pixel height and width of the screen. We introduce a new scalable technique, MultiLayerMatrix, to visualize very large matrices by breaking them into multiple layers. In our technique, the top layer shows the relationships between different groups of clustered data while each sub-layer shows the relationships between nodes in each group as needed. This process can be applied iteratively to create multiple sub-layers for very large datasets. We illustrate the usefulness of MultiLayerMatrix by applying it to a network representing similarity measures between 2,048 characters in the Asteraceae taxonomy, a rich dataset that describes characteristics of species of flowering plants.We also discuss the scalability of our technique by investigating its effectiveness on a large synthetic dataset with 20,000 columns by 20,000 rows.en_US
dc.description.sectionheadersNetworksen_US
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)en_US
dc.identifier.doi10.2312/eurova.20161125en_US
dc.identifier.isbn978-3-03868-016-1en_US
dc.identifier.issn-en_US
dc.identifier.pages55-59en_US
dc.identifier.urihttps://doi.org/10.2312/eurova.20161125en_US
dc.identifier.urihttps://diglib.eg.org/items/89de1bb7-58c3-4d94-b650-a28369953958
dc.publisherThe Eurographics Associationen_US
dc.subjecten_US
dc.titleMultiLayerMatrix: Visualizing Large Taxonomic Datasetsen_US
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