Multivariate Data Visualization Anilkumar Patro
Overview <ul><li>Visualization…What? </li></ul><ul><li>Visualization…How? </li></ul><ul><li>Multivariate Visualization…Huh...
Visualization
Visualization <ul><li>Visualization “hopefully” makes it easier for humans to discover hidden facts (extract information) ...
Creating Visualizations Data Source Graphical Mapping Display User COLOR SIZE POSITION STYLE ORIENTATION TEXTURE SHAPE
Scientific Visualization <ul><li>Visual representations of data represent objects in 1D, 2D or 3D object space </li></ul>
Information Visualization <ul><li>Represents concepts and relationships that do not necessarily have a counterpart in the ...
Multivariate Visualization <ul><li>Problems </li></ul><ul><ul><li>How to effectively present more than 3 dimensions of inf...
Taxonomy Geometric Projection Iconographic Pixel Oriented Hierarchical
Summary <ul><li>A (2D) picture is worth a thousand words.  </li></ul><ul><li>An nD picture is worth … </li></ul><ul><li>Tr...
Thank You Questions???
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Multivariate Data Visualization

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  • Multivariate Data Visualization

    1. 1. Multivariate Data Visualization Anilkumar Patro
    2. 2. Overview <ul><li>Visualization…What? </li></ul><ul><li>Visualization…How? </li></ul><ul><li>Multivariate Visualization…Huh? </li></ul>
    3. 3. Visualization
    4. 4. Visualization <ul><li>Visualization “hopefully” makes it easier for humans to discover hidden facts (extract information) contained in the data </li></ul><ul><ul><li>“ Discover” </li></ul></ul><ul><ul><ul><li>Users do not know what exactly they are looking for </li></ul></ul></ul><ul><ul><li>“ Hidden Facts” </li></ul></ul><ul><ul><ul><li>Trends or patterns </li></ul></ul></ul><ul><ul><ul><li>Hotspots / anomalies </li></ul></ul></ul><ul><ul><ul><li>Comparisons </li></ul></ul></ul><ul><ul><ul><li>Form Hypotheses </li></ul></ul></ul>
    5. 5. Creating Visualizations Data Source Graphical Mapping Display User COLOR SIZE POSITION STYLE ORIENTATION TEXTURE SHAPE
    6. 6. Scientific Visualization <ul><li>Visual representations of data represent objects in 1D, 2D or 3D object space </li></ul>
    7. 7. Information Visualization <ul><li>Represents concepts and relationships that do not necessarily have a counterpart in the physical world </li></ul><ul><li>May describe multiple related attributes </li></ul>
    8. 8. Multivariate Visualization <ul><li>Problems </li></ul><ul><ul><li>How to effectively present more than 3 dimensions of information in a visual display with 2 (to 3) dimensions? </li></ul></ul><ul><ul><li>How to effectively visualize “inherently abstract” data? </li></ul></ul><ul><ul><li>How to effectively visualize very large, often complex data sets? </li></ul></ul><ul><ul><li>How to effectively display results – when you don’t know what those results will be? </li></ul></ul><ul><li>Goals </li></ul><ul><ul><li>More than 3 dimensions of data simultaneously </li></ul></ul><ul><ul><li>Support “fuzzyness” (similarity queries, vector space, tolerance ranges) </li></ul></ul><ul><ul><li>Support exploratory, opportunistic, “what-if” queries </li></ul></ul><ul><ul><li>Allow identification of interesting data properties through pattern recognition </li></ul></ul><ul><ul><li>Explore various dimensions without losing overview </li></ul></ul>
    9. 9. Taxonomy Geometric Projection Iconographic Pixel Oriented Hierarchical
    10. 10. Summary <ul><li>A (2D) picture is worth a thousand words. </li></ul><ul><li>An nD picture is worth … </li></ul><ul><li>Trade-off between amount of information, simplicity, and accuracy </li></ul><ul><li>It is often hard to judge what users will find intuitive and how a visualization will support a particular task </li></ul>
    11. 11. Thank You Questions???

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