UMD CMSC 838S - Exploring High-D Spaces with Multiform Matrices and Small Multiples

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Slide 1Slide 2OutlineContributionMultiform Bivariate small MultipleSlide 6Multiform Bivariate MatrixMultiform Bivariate Small Multiple and MatrixFind most significant relations between attributesGenerate an ordering of attributesSlide 11Select subspaceCritiquesDemoTHANKS!Never forget your primary wealth, your and your family’s health, it will be your hope and your family’s hope for ever.Exploring High-D Spaces with Multiform Matrices and Small Multiplesby Alan MacEachren etc.Presenter:Xu LiuMing LuoOutlineContributionMain ideasDemosCritiquesContributionGeoVISTA development environmentA general framework, ready for combination of any information visualization formsDynamically visualize information with user interactionExplore high dimension by univariate and bivariate relations between attributesFind most significant relations between attributesMultiform Bivariate small MultipleMultiform Bivariate small MultipleO OOMultiform Bivariate MatrixMultiform Bivariate Small Multiple and MatrixMultiple and matrix are designed as generic JavaBean components, providing a Java interface through which any bivariate representation form (instantiated as a JavaBean) can communicate.Visualization is updated dynamically with the user’s interactionFind most significant relations between attributesMaximum conditional entropy between each pair of attributesGenerate an ordering of attributes, which keeps a hierarchical clusteringSelect subspace interactively or automaticallyGenerate an ordering of attributesA node is an attributeAn edge is the maximum conditional entropy between two attributesGenerate an ordering of attributesA node is an attributeAn edge is the maximum conditional entropy between two attributesSelect subspaceCritiquesGrid-based space-filling displayIt can not show more information than a simple scatterplot (maybe worse)Color legend needs trainingRare visualization tools for more than 2 variablesDemoBasic Usage ConditioningBi-variable: you see it’s hard to select colorsPCP: a good alternation for multi variable scatter plotBad example: who can tell me what’s going on here?THANKS!You may find the demo at


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UMD CMSC 838S - Exploring High-D Spaces with Multiform Matrices and Small Multiples

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