1 / 42

Visualization

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

trista
Download Presentation

Visualization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

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

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

  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 • 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 J LeBlanc et al.: Exploring n-dimensional databases. In Proc. Visualization 90, pages 230-239, 1990.

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

  25. Stacked Displays Mosaic display in Orange.

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

  27. 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 Interaction Techniques

  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 ofVisualization & Data Mining • Visualization techniques can be applied before (or independently) of DM • DM can be used to find patterns (or data subsets) that are further visualized • DM is interactive, users use visualization to guide the pattern search • 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

More Related