1. A Rank-by-Feature Framework for Interactive Multi-dimensional Data Exploration Jinwook Seo and Ben Shneiderman Human-Computer Interaction Lab. & Department of Computer Science University of Maryland, College Park

2. Hierarchical Clustering Explorer (HCE)

3. “ HCE enabled us to find important clusters that we don ’ t know about yet. ”

4. Goal: Find Interesting Features in Multidimensional Data Finding correlations, clusters, outliers, gaps, … is difficult in multidimensional data –Cognitive difficulties in >3D Therefore utilize low-dimensional projections –Perceptual efficiency in 1D and 2D –Use Rank-by-Feature Framework to guide discovery

5. Do you see anything interesting?

6. Do you see any interesting feature?

7. Correlation … What else?

8. Outliers He Rn

9. Demonstration Breakfast Cereals –77 cereals –8 dimensions (or variables) : sugar, potassium, fiber, protein, etc. US counties census data –3138 counties –14 dimensions : population density, poverty level, unemployment, etc.

10. Low-dimensional Projections Techniques –General combination of variables for an axis –Axis parallel a variable for an axis Number of projections Interface for Exploration X 1 +2X 2 -2X 1 +X 2 X1X1 X3X3

11. Exploration by Projections XGobi, GGobi – Scatterplot Browsing www.ggobi.orgwww.research.att.com/areas/stat/xgobi/

12. Exploration by Projections Spotfire DecisionSite – Scatterplots www.spotfire.com

13. Exploration by Projections XGobi, GGobi – Grand Tour

14. Exploration by Projections XmdvTool – Scatterplot Matrix Worcester Polytechnic Institute

15. Dimension selection tool Corrgram by Michael Friendly Square Matrix Display in GeoVISTA studio by Alan M. MacEachren

16. Exploration by Projections Spotfire DecisionSite – View Tip orders scatterplots

17. Design Considerations Hard to interpret arbitrary linear projections  Axis-parallel projections Interestingness depends on applications  Incorporate users’ interest Overview of all possible projections Rapid change of axis

18. Demonstration Breakfast Cereals –77 cereals –11 dimensions (or variables) : sugar, potassium, fiber, protein, etc. US counties census data –3138 counties –14 dimensions : population density, poverty level, unemployment, etc.

19. Rank-by-Feature Framework: 1D Ranking Criterion Rank-by-Feature Prism Score List Manual Projection Browser

20. Rank-by-Feature Framework: 2D Ranking Criterion Rank-by-Feature Prism Score List Manual Projection Browser

21. Ranking Criterion: Pearson correlation (0.996, 0.31, 0.01, -0.69) Ranking Criterion: Uniformity (entropy) (6.7, 6.1, 4.5, 1.5) A Ranking Example 3138 U.S. counties with 17 attributes

22. Ongoing and Future Work Identify & implement more ranking criteria –Gaps, outliers, etc. Ranking based on users ’ selection of items –Separability of the selected items –Ranking by using only the selected items Scalability Issue –How to handle a large number of dimensions –Grouping by clustering dimensions –Filtering uninteresting entries in the prism

23. More about HCE In collaboration and sponsored by Eric Hoffman: Children’s National Medical Center Freely downloadable at www.cs.umd.edu/hcil/hce www.cs.umd.edu/hcil/hce Version 3.0 beta, May 2004 About 2,000 downloads since April 2002 Licensing to ViaLactia Biosciences (NZ) Ltd.

24. More Applications? Try HCE and the Rank-by-Feature Framework with your problems and data Join the case studies on the use of HCE and the Rank-by-Feature Framework Welcome suggestions and comments

25. Thank you !