Visualization

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.
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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
Interaction Distortion Technique
Visualization Technique
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Data One-Dimensional
R Bellazzi Mining Biomedical Time Series by
Combining Structural Analysis and Temporal
Abstractions, In Proc. of AMIA 1998.
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Data Two-Dimensional
MineSets Map Visualizer.
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Data Multi-Dimensional
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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/infovi
z/technologies.html
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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/infovi
z/technologies.html
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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/infovi
z/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 _at_ vlado.fmf.uni-lj.si/p
ub/networks/pajek/
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Data Tree Hierarchies
Unix home directory
Selected detail
Kleiberg et al. Botanic Visualization of Huge
Hierarchies, In InfoVis, 2001.
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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
Interaction Distortion Technique
Visualization Technique
16
Standard 2D/3D

• x-y (x-y-z) plots
• bar charts
• line graphs
• histograms
• maps

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Standard 2D/3D

• x-y (x-y-z) plots
• bar charts
• line graphs
• histograms
• maps

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Standard 2D/3D

• x-y (x-y-z) plots
• bar charts
• line graphs
• histograms
• maps

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Geom.-Transformed Displays

• includes several classes of visualizations
• projection pursuit, finding interesting
  transformations of multi-dim data set
• scatterplot matrix
• parallel coordinates

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Iconic Displays
W Horn et al. Metaphor graphics to visualize ICU
data over time, In IDAMAP 1998.
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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.
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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.
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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.
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Stacked Displays
Decision table visualization from SGIs MineSet
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Stacked Displays
Mosaic display in Orange.
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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
Interaction Distortion Technique
Visualization Technique
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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

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Distortion
GW Furnas Generalized Fisheye Views, Human
Factors in Computing Systems CHI 86 Conference
Proceedings, 16-23. 1986.
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Distortion
From M Grobelnik, P Krese, D Mladenic Project
Intelligence (http//pi.ijs.si)
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Distortion
From M Grobelnik, P Krese, D Mladenic Project
Intelligence (http//pi.ijs.si)
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Distortion
From M Grobelnik, P Krese, D Mladenic Project
Intelligence (http//pi.ijs.si)
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Brushing Linking
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Integration ofVisualization 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

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Regression Tree
Regression tree visualization in SGIs MineSet.
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Classification Tree
Classification tree visualization in Orange.
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Brushing Trees Scatter Plots
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Sieve Diagram (Classification)
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Nomograms
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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

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

• Clarity of presentation
• Aesthetics
• Navigation Interaction
• In data with many dimensions, tools are needed to
  find only interesting visualizations