1. Mutlidimensional Detective Alfred Inselberg Streeable, Progressive, Mutlidimensional Scaling Matt Williams, Tamara Munzner Rylan Cottrell

2. Mutlidimensional Detective Transformation of multivariate relations into 2-D patterns A discovery process for visual data mining

3. Parallel Coordinates Visualize without loss of information. Properties Low complexity. # Dimensions = # Variables Works for any # of dimensions Variables treated uniformly N - dimensional Object Recognized under projective transformations. Conveys information on the properties Based on rigorous math/algo results.

4. DON’T PANIC

5. Data 473 batches of processors 16 variables X1 - % of yield X2 - quality X3... X12 - are different types of defects X13... X16 - denote a physical parameter

7. Maximize yield and quality

9. Batches with the highest quality

10. Portion of Slovenia

11. Satellite Data B1..B5, B7 - Intensity of reflected electromagnetic wavelengths B6 - Intensity of emitted thermal IR from object X,Y - Map Position

12. Portion of Slovenia

15. Multidimensional Scaling Create a low dimensional layout of data Distance between points best represents the points in higher dimensional data.

17. Steerable, Progressive MDS Problem - No Interactive exploration of high-dimensional data sets Unreasonable time cost associated data sets that are large in dimensions and points Steering - focuses computational power

19. Layout a random subset of the data set

20. Divide bin in two Apply high-dimensional distance

21. A new random subset of points are added into the layout

22. Focus is placed on user defined bin

23. A new subset of random points selected from the unplaced points in the selected region are added

24. The process is repeated as the user refines his selection

25. MDSteer Standard Layout (Morrison) 50,000 data points

26. http://www.cs.ubc.ca/~tmm/papers/mdsteer/videos/MDSteer1.mov

27. Standard Layout (Morrison) MDSteer 40,000 data points

28. http://www.cs.ubc.ca/~tmm/papers/mdsteer/videos/MDSteer2Combined.mov

31. References Alfred Inselberg: The Automated Multidimensional Detective. INFOVIS 1997: 107-114 Alfred Inselberg: Parallel Coordinates: Visuak Multidimensional Geometry and its Applications. 2004. http://www.math.tau.ac.il/~aiisreal/index_files/lect-pdf/lect-intro.pdf http://www.math.tau.ac.il/~aiisreal/index_files/lect-pdf/lect-intro.pdf Matt Williams, Tamara Munzner: Steerable, Progressive Multidimensional Scaling. INFOVIS 2004: 57-64. Project website http://www.cs.ubc.ca/labs/imager/tr/2004/mdsteer/INFOVIS 2004 Matt Williams: QuestVis and MDSteer: The Visualization of High-Dimensional Environmental Sustainability Data. MSc. Thesis. 2004.