Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

1. Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

2. Our goal • A parallel coordinates visualization that: • Employs Focus+Context • Handles outliers • Renders effectively Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

3. Overview • Motivation • Abstraction, Focus+Context • Outliers • Workflow • Binning • Context • Benefits • Bonus! • Results and conclusions Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

4. Parallel Coordinates • Insight into multidimensional data • Correlations, Groups, Outliers Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

5. Parallel Coordinates • Insight into multidimensional data • Correlations, Groups, Outliers Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

6. Parallel Coordinates • Insight into multidimensional data • Correlations, Groups, Outliers Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

7. Large data visualization • Large data cause clutter in visualization • 16.000 records Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

8. Large data visualization • Transparency used to decrease clutter • 16.000 records Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

9. Large data visualization • Transparency used to decrease clutter ? • 32.000 records Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

10. Large data visualization • Transparency used to decrease clutter ?? • 64.000 records Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

11. Large data visualization • Transparency used to decrease clutter ??? • 100.000 records Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

12. Large data visualization • Transparency used to decrease clutter ??? • Do these records belong to the main trend? Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

13. Data abstraction • Density-based representation of data • Trends are clearly visible 16 bins Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

14. Related work • Hierarchical Parallel Coordinates(Fua et al., 1999) • Visual representationof clusters • Smooth transparency • Cluster centersemphasized Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

15. Related work • Revealing Structure within Clustered Parallel Coordinates Displays (Johansson et al., 2005) • Textures, density • Transfer functions • Clusters • Outliers Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

16. Outliers • Different, sparse, rare • Why should we care? • Investigation (special cases in simulations…) • Security (network intrusion, suspicious activity…) • Detect errors in data acquisition • Outliers can be considered in: • Data space • Screen space Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

17. Outliers Outliers are like kids. If you leave them unattendedthey either get lostor they break stuff. Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

18. Outliers • Avoid losing them in visualization • e.g. due to transparency or abstraction • Improve data abstraction or F+C • e.g. remove outliers from clustering Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

19. Workflow Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

20. Workflow Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

21. Step 1: Binning • 2D binning • Density-based rep. • Screen-oriented • Low memory demandscompared to nD • Every segmentseparately • Result = bin map Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

22. Benefits of binning? • Operations no longer dependon the size of the input • Information is preserved • Variable precision of binning • Variable precision of visual output • Fine binning does not destroy details • Larger bins can be producedfrom finer bins 128x128 bins Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

23. Step 2: Outlier detection • Various criteria can be employed • e.g. isolated bins, median filter … 64x64 bin map 32x32 bin mapmedian filter 32x32 bin mapisolated bins Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

24. Step 3: Generating Context • Outliers → opaque lines • Binned trends → quads • Population mapped to color intensity • No blending • Low visual complexity • Rendering order according to population 8 bins Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

25. Step 4: Add Focus 8 bins Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

26. Benefits • Operations performed on bin maps • Reduced complexity • Results coherent with visual output • More operations feasible – e.g. Clustering • Outliers handled separately • Increased information value • Clearer context • Output-sensitive implementation • View divided into layers and segments Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

27. Results • Large data can be rendered and explored • 3 millions records, 16 dimensions, 32 bins • Binned in 30 sec, rendered instantly (3Ghz,64bit) Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

28. BONUS! Clustering Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

29. Clustering, step 0 • Apply Gaussian to smooth out the bin map • Segmentation data, Green vs Darkness Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

30. Clustering, further steps • Start with the highest population • Decrease the population threshold • Old clusters grow • New clusters emerge 50% 20% 10% 0% Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

31. Clustering results R B G D S H Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

32. Clustering results R B G D S H Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

33. Clustering results R B G D S H Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

34. Clustering results R B G D S H Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

35. Conclusions • Data abstraction based on density rep. • Data operations - outlier detection, clustering • Focus+Context • Variable context precision • Outliers preserved • Much clearer view for large data • Screen-oriented and output-sensitive • Interactive visualization of large data Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

36. Acknowledgements • K-Plus • Vega grant 1/3083/06. • AVL List GmbH - data • Juergen Platzer • Prof. Peter Filzmoser • Harald Piringer • Michael Wohlfahrt Outlier-Preserving Focus+Context Visualization in Parallel Coordinates

37. Thank you for your attention!