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Thank you for coming here!

Thank you for coming here!. Purpose of Experiment. Compare two visualization systems. You will play with one of them. . What will you do?. Learn a multidimensional visualization system; Use it to find features of a data set and record your result; A quick after-experiment feedback.

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Thank you for coming here!

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  1. Thank you for coming here!

  2. Purpose of Experiment • Compare two visualization systems. • You will play with one of them.

  3. What will you do? • Learn a multidimensional visualization system; • Use it to find features of a data set and record your result; • A quick after-experiment feedback.

  4. Schedule • First, I will present ... Multidimensional data Hierarchical Parallel Coordinates Brushing Feature finding Introduce the visualization system

  5. Schedule • Then, You will do ... Experiment: -Find features of a given data set using the visualization system -Record the features you find Fill feedback form.

  6. Outline • Multidimensional Data • How to represent multidimensional data • Parallel Coordinates • Hierarchical Clustering • Hierarchical Parallel Coordinates • Brushing Operation • Feature Finding

  7. Multidimensional DataExample: Iris Data Scientists measured the sepal length, sepal width, petal length, petal width of many kinds of iris...

  8. Multidimensional DataExample: Iris Data

  9. Outline • Multidimensional Data • How to represent multidimensional data • Parallel Coordinates • Hierarchical Clustering • Hierarchical Parallel Coordinates • Brushing Operation • Feature Finding

  10. Parallel Coordinates One-Dimensional Data (1.6) 2 1

  11. Parallel Coordinates 4-Dimensional Iris Data Set

  12. 3.5 5.1 1.4 0.2

  13. Outline • Multidimensional Data • How to represent multidimensional data • Parallel Coordinates • Hierarchical Clustering • Hierarchical Parallel Coordinates • Brushing Operation • Feature Finding

  14. Hierarchical ClusteringHierarchical Cluster Tree A cluster tree

  15. Hierarchical ClusteringMean, Extent y P1( 3, 6) p2( 5, 5) Mean Point of C1 = (P1+P2)/2 = (4, 5.5) Extent of C1: x:[3, 5] y:[5, 6] P1 P2 C1 x

  16. Outline • Multidimensional Data • How to represent multidimensional data • Parallel Coordinates • Hierarchical Clustering • Hierarchical Parallel Coordinates • Brushing Operation • Feature Finding

  17. Outline • Multidimensional Data • How to represent multidimensional data • Parallel Coordinates • Hierarchical Clustering • Hierarchical Parallel Coordinates • Brushing Operation • Feature Finding

  18. Brushing Brushing - Highlighting part of the clusters to distinguish them from the other clusters.

  19. Outline • Multidimensional Data • How to represent multidimensional data • Parallel Coordinates • Hierarchical Clustering • Hierarchical Parallel Coordinates • Brushing Operation • Feature Finding

  20. Feature Finding Feature - Anything you find from the data set. Cluster - A group of data items that are similar in all dimensions. Outlier - A data item that is similar to FEW or No other data items.

  21. Other features You can record anything else you find with the help of the visualization system.

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