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

8. Multidimensional Data Example: 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 2 (1.6)

11. Parallel Coordinates 4-Dimensional Iris Data Set

12. 5.1 3.5 1.4 0.2

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

15. Hierarchical Clustering Hierarchical Cluster Tree A cluster tree

16. Hierarchical Clustering Mean, Extent P2 P1 C1 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] x y

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

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

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

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

34. 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.

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