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Interactive Exploration of Multidimensional Data

Interactive Exploration of Multidimensional Data. By: Sanket Sinha Nitin Madnani. Is It Really That Common ?. You Bet: Demographics Economics Census Microarray Gene Expression Engineering Psychology Health. I can ’ t see it, I tell ya !. Visualization challenges for >= 3D:

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Interactive Exploration of Multidimensional Data

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  1. Interactive Exploration of Multidimensional Data By: Sanket Sinha Nitin Madnani

  2. Is It Really That Common ? • You Bet: • Demographics • Economics • Census • Microarray Gene Expression • Engineering • Psychology • Health

  3. I can’t see it, I tell ya ! • Visualization challenges for >= 3D: • Relationship comprehension is difficult • Discovering outliers, clusters and gaps is almost impossible • Orderly exploration is not possible with standard visualization systems • Navigation is cognitively onerous and disorienting (3D) • Occlusion (3D)

  4. 1D : Histograms 2D : Scatterplots Standard Solution • Can you say “Pro-jek-shun” ? • Use lower dimensional projections of data:

  5. But there are so many ! • For 13 dimensions (columns) : • Number of histograms = 13 • Number of scatterplots = C(13,2) = 78 • Must examine a series of these to gain insights • Unsystematic == Inefficient • Must have order !

  6. Introducing Rank-by-feature • Allows projections to be examined in an orderly fashion • A powerful framework for interactive detection of: • Inter-dimension relationships • Gaps • Outliers • Patterns

  7. How does it work ? • Framework defines ranking criteria for 1D & 2D projections • User selects criterion of interest • All projections are scored on the criterion and ranked • User examines projections in the order recommended • Eureka* !! *Disclaimer: All users may not be able to make life-altering discoveries

  8. Ranking Criteria - 1D • Normality: Indicative of how “Gaussian” the dataset is • Uniformity: How “uniform” is the dataset ?(How high is the entropy ?) • Outliers: The number of potential outliers in the dataset • Gap: The size of the biggest gap • Uniqueness: Number of unique data points

  9. Ranking Criteria - 2D • Linear Correlation: Pearson’s correlation coefficient • LSE: Least Square Error from the optimal quadratic curve fit • Quadracity: Quadratic coefficient from fitting curve equation • Uniformity: Joint entropy • ROI: Number of items in a Region Of Interest • Outliers: Number of potential outliers

  10. Put A Demo Where Your Mouth Is !

  11. HCE Overview

  12. The Input Dialog Box Perform Filtering & Normalization

  13. Histogram Ordering

  14. Scatterplot Ordering

  15. Tabular View of Data Select specific data records and annotate if needed

  16. Questions/Critiques • What does “outlierness” mean? • Cannot identify datapoints in histogram or scatterplot browser without switching to table view • Especially in ROI • How to intuitively interpret: • Outliers in 2D • LSE • Quadracity

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