1. Visualization Blaz Zupan Faculty of Computer & Info Science University of Ljubljana, Slovenia

2. Visual Data Mining Basic idea –visual presentation of the data –gain insight & generate hypothesis –draw conclusions –directly interact with data Include human in the data exploration process –use her/his flexibility –creativity –general knowledge

3. Benefits of Visualization involvement of the user results are intuitive –no need for understanding complex mathematical or statistical algorithms or parameters provision of qualitative overview of the data –can isolate specific patterns for further quantitative analysis can deal with non-homogenous, noisy data

4. Visual Exploration Paradigm Overview first, zoom & filter, and then details on demand.

5. Visual Exploration Paradigm Overview first, zoom & filter, and then details on demand.

6. Classification one-dimensional two-dimensional multi-dimensional text, web content networks other (e.g. algorithms/software,...) Data Type Interaction & Distortion Technique Visualization Technique Standard Projection Filtering Zoom Distortion Link & Brush Standard 2D/3D Display Geometrically Transformed Display Iconic Display Dense Pixel Display Stacked Display from D Keim & M Ward: Visualization, in Intelligent Data Analysis, M Berthold & DJ Hand (eds), Springer, 2003.

7. Data: One-Dimensional R Bellazzi: Mining Biomedical Time Series by Combining Structural Analysis and Temporal Abstractions, In Proc. of AMIA 1998.

8. Data: Two-Dimensional MineSet’s Map Visualizer.

9. Data: Multi-Dimensional

10. Data: Text Galaxies visualization Uses the “night sky” visualization to represent a set of documents One document – one star Stars clustered together represent related documents Includes analytical tools to investigate groups and time- based trends, query contents From Inspire (TM) Software, see www.pnl.gov/infoviz/technologies.html

11. Data: Text ThemeView (TM) Topics or themes of text documents shown in relief map of a natural terrain The height of a peek relates to the strength of the topic From Inspire (TM) Software, see www.pnl.gov/infoviz/technologies.html

12. Data: Text Theme River (TM) Identification of time related trends and patterns Themes represented as colored streams The width of the stream relates to the collective strength of a theme From Inspire (TM) Software, see www.pnl.gov/infoviz/technologies.html

13. Data: Networks E. coli metabolic network (colors denote predominant biochemical class of metabolites) Ravasz et al., Science 297, 30 Aug 2002. S. cerevisiae gene interaction network Tong et al., Science 303, 6 Feb 2004. V Batagelj, A Mrvar: Pajek @ vlado.fmf.uni-lj.si/pub/networks/pajek/

14. Data: Tree Hierarchies Unix home directorySelected detail Kleiberg et al.: Botanic Visualization of Huge Hierarchies, In InfoVis, 2001.

15. Classification one-dimensional two-dimensional multi-dimensional text, web content networks other (e.g. algorithms/software,...) Data Type Interaction & Distortion Technique Visualization Technique Standard Projection Filtering Zoom Distortion Link & Brush Standard 2D/3D Display Geometrically Transformed Display Iconic Display Dense Pixel Display Stacked Display

16. Standard 2D/3D x-y (x-y-z) plots bar charts line graphs histograms maps

17. Standard 2D/3D x-y (x-y-z) plots bar charts line graphs histograms maps

18. Standard 2D/3D x-y (x-y-z) plots bar charts line graphs histograms maps

19. Geom.-Transformed Displays includes several classes of visualizations projection pursuit, finding “interesting transformations ” of multi-dim data set scatterplot matrix parallel coordinates

20. Iconic Displays W Horn et al.: Metaphor graphics to visualize ICU data over time, In IDAMAP 1998.

21. Dense Pixel Displays DA Keim et al.: Recursive Pattern: A technique for visualizing very large amounts of data Proc. Visualization 95, pages 279-286, 1995.

22. Dense Pixel Displays Ankerst et al.: Circle Segments: A technique for visually exploring large multidimensional data sets. In Proc. Visualization 96, Hot Topic Session, 1996.

23. Stacked Displays J LeBlanc et al.: Exploring n-dimensional databases. In Proc. Visualization 90, pages 230-239, 1990. an example is dimensional stacking embed one coordinate system within the other e.g. two attributes in one system, then another two when drilling down

24. Stacked Displays Decision table visualization from SGI’s MineSet

25. Stacked Displays Mosaic display in Orange.

26. Classification one-dimensional two-dimensional multi-dimensional text, web content networks other (e.g. algorithms/software,...) Data Type Interaction & Distortion Technique Visualization Technique Standard Dynamic Projection Filtering Zoom Distortion Link & Brush Standard 2D/3D Display Geometrically Transformed Display Iconic Display Dense Pixel Display Stacked Display

27. Interaction Techniques Dynamic projection –dynamically change the projections to explore multi-dimensional data sets –projection pursuit, which finds well-separated clusters in scatterplot Interactive Filtering –browsing, can be difficult for big data sets –querying, need to specify a subset Zooming Distortion –e.g., fisheye view Brushing and linking –requires well-integrated system for visualization –selection from one visualization is fed into another one, selected instances highlighted in some way

28. Distortion GW Furnas: Generalized Fisheye Views, Human Factors in Computing Systems CHI ‘86 Conference Proceedings, 16-23. 1986.

29. Distortion From M Grobelnik, P Krese, D Mladenic: Project Intelligence (http://pi.ijs.si)

30. Distortion From M Grobelnik, P Krese, D Mladenic: Project Intelligence (http://pi.ijs.si)

31. Distortion From M Grobelnik, P Krese, D Mladenic: Project Intelligence (http://pi.ijs.si)

32. Brushing & Linking

33. Integration of Visualization & Data Mining 1.Visualization techniques can be applied before (or independently) of DM 2.DM can be used to find patterns (or data subsets) that are further visualized 3.DM is interactive, users use visualization to guide the pattern search 4.Visualization of data mining models

34. Regression Tree Regression tree visualization in SGI’s MineSet.

35. Classification Tree Classification tree visualization in Orange.

36. Brushing: Trees & Scatter Plots

37. Sieve Diagram (Classification)

38. Nomograms

39. Intelligent Data Visualization Use an established visualization technique, but search for –interesting subset of attributes –interesting subset of data instances –interesting projection (how to use selected attributes in visualization) All these to find “interesting” visualization Removes the burden for the user to find such visualizations by hand

40. Arrangement for Circle Segments M Ankerst: Visual data mining with pixel-oriented techniques, In Proc. KDD, 2001.

41. VizRank

42. Conclusion Clarity of presentation Aesthetics Navigation & Interaction In data with many dimensions, tools are needed to find only “interesting” visualizations