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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