Information%20Visualization

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Information Visualization
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Lecture Outline

• Overview of information visualization
• The role of visualization in the process of data
  mining
• The patterns being sought clusters and outliers
• Issues when visualizing higher dimensional
  relationships
• Criteria for comparison
• A range of visualization techniques for
  exploratory data analysis

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Information Visualization

• A conjunction of a number of fields
• Data Mining
• Cognitive Science
• Graphic Design
• Interactive Computer Graphics

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Information Visualization

• Information Visualization attempts to use visual
  approaches and dynamic controls to provide
  understanding and analysis of multidimensional
  data
• The data may have no inherent 2D or 3D semantics
  and may be abstract in nature.
• There is no underlying physical model.
• Much of the data in databases is of this type

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Role of Information Visualization

• Acts as an exploratory tool
• Useful for identifying subsets of the data
• Structures, trends and outliers may be identified
• Statistical tests tend to incorporate isolated
  instances into a broader model as they attempt to
  formulate global features
• There is no requirement for an hypothesis, but
  the techniques can also support the formulation
  of hypotheses if wanted

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Integrating Visualization WithData Mining

• There are four possible approaches
• Use the visualization technique to present the
  results of the data mining process.
• Use visualization techniques as complements to
  the data mining process.
• They complement and increase understanding in a
  passive way.

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Integrating Visualization WithData Mining

• Use visualization techniques to steer the data
  mining process.
• The visualization aids in deciding the
  appropriate data mining technique to use and
  appropriate subsets of the data to consider.
• Apply data mining techniques to the visualization
  rather than directly to the data.
• The idea is to capture the essential semantics
  visually then apply the data mining tools.

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The Process of Knowledge Discovery in Databases
(a.k.a. Data Mining)
DataSelection
Cleaning Enrichment
Coding
Data mining
Reporting

- clustering
-domain consistency
- segmentation
-de-duplication
- prediction
-disambiguation
Information Requirement
Action
Feedback
External data
Operational data
The Knowledge Discovery in Databases (KDD)
process (AdZ1996)
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Visualization in the Context of the Data Mining
Process

• Visualization tools can potentially be used at a
  number of steps in the DM process. But
• the same tools may not be appropriate at each
  step
• how they will be used may be different

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Visualization in the Context of the Data Mining
Process

• In general, it is not important whether data
  visualization is the first step in the process or
  not
• the feedback loop which moves the process forward
  may be commenced by either a visualization or a
  query

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Visualization in the Context of the Data Mining
Process

• some visualizations, (e.g. see slide 25) require
  an initial query to generate a visualization
• this is an example of a complementary approach
• questions generate visualizations, which may
  prompt further questions or generate hypotheses

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Motivations for Visualization

• The human visual system is extremely good at
  recognizing patterns
• it is quicker and easier to understand visual
  representations than to absorb information from
  language or formal notations.
• Exploratory visualization assists in
• identifying areas of interest
• identifying questions which might usefully be
  asked

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Motivations for Visualization

• i.e. a relevant or revealing visualization of
  either part or all of a data set, may suggest
  useful questions and/or hypotheses to the
  analyst. These can then be confirmed by more
  rigorous approaches
• e.g. some clustering techniques require an
  initial estimate of the number of clusters
  present in the data
• visualization techniques can assist in this
  estimation

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Criteria for Comparison of Visualization Tools

• Number of dimensions that can be represented
• Number of data items that can be handled
• Ability to handle categorical and other
  non-numeric data types
• Ability to reveal patterns
• Ease of use
• Learning Curve (to what degree is the technique
  intuitive)

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Examples - Scatterplot

• Each pair of features (i.e. fields of records) in
  a multidimensional database is graphed as a point
  in two dimensions (2D)
• This straightforward graphing procedure produces
  a simple scatterplot - a projection of the
  multidimensional data into 2D

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Examples - Scatterplot

• The scatterplots of all pair-wise combinations of
  features are arranged in a matrix
• The figure on the following slide illustrates a
  scatter plot matrix of 3D from a study of
  abrasion loss in tyres. The features are
  hardness, tensile-strength, abrasion-loss
  Tie1989
• Each sub-graph gives insight into the
  relationship between a pair of features

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Scatterplot Matrix

• Scatterplot matrix of abrasion loss data Tie1989

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Possible Problems With Scatterplots

• Everitt Eve78, p. 5 gives two reasons why
  scatter plots can prove unsatisfactory
• if number of features is greater than 10, the
  number of plots to be examined is very large
• this is just as likely to lead to confusion as
  to knowledge of the structures in the data.
• structures existing in multidimensional data set
  do not necessarily appear in the 2D projections
  of the features represented in scatterplots (see
  next slide)

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Possible Problems With Scatterplots

• Despite these potential problems, variations on
  the scatterplot approach are the most commonly
  used of all the visualization techniques

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Scatterplots Recognizing High-dimensional
Structures - 1

• A structure which appears as a cluster in a 2D
  projection may in fact be a pipe in 3D
• a pipe is a structure in 3D that looks like a rod
  or pipe when viewed in a 3D representation

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Scatterplots Recognizing High-dimensional
Structures - 1

• While the pipe is easily identifiable in a 3D
  display only projections of it will appear in the
  2D components of the scatterplot matrix
• depending of the orientation of the pipe in 3D,
  it may not appear as an obvious cluster, if at all

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Scatterplots Recognizing High-dimensional
Structures - 1

• Equivalent structures can exist in higher
  dimensions, e.g. a cluster in 5D might be a
  pipe in 6D
• the appearance of high-D structures in lower-D
  projections depends on the luck and skill of the
  analyst in choosing the projections, and on the
  alignment of the structures to the axes

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Scatterplots recognizing high-dimensional
structures - 2
Random(Uniform)
May be a plane in 3D
A cluster in 2D
May be a pipe in 3D (or a cluster in 3D)
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Example Tool Spotfirehttp//www.spotfire.com/
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Example Tool Spotfirehttp//www.spotfire.com/

• The user interacts with data by choosing which
  features will form the horizontal and vertical
  axes
• Other features can be represented by color
• this is an example of using the richness of
  visual representations to provide more
  information to the user. As well as 2D spatial
  position, other modes such as colour, size, shape
  and even sound can be used to convey information
  about high-dimensional data

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Example Tool Spotfirehttp//www.spotfire.com/

• On the previous slide, the data set contains a
  3D cluster
• The cluster can seen, with its centre at around
  (20, 74)
• all the points in the cluster are red, showing
  that its a 3D cluster

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Example Tool DBMinerhttp//www.dbminer.com/
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Example Tool DBMinerhttp//www.dbminer.com/

• DBMiner is an integrated data mining tool
• It employs a data visualization known as a data
  cube (see On-Line Analytic Processing - OLAP)

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Example Tool DBMinerhttp//www.dbminer.com/

• After creating a data cube, user can apply a
  variety of data mining techniques to analyze the
  data further, including
• association, classification, prediction and
  clustering, etc.
• The figure on the preceding slide shows a data
  cube for a data set which has 3D cluster of data
  instances in a 3D space

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Examples Parallel Coordinates - 1

• Uses the idea of mapping a point in a
  multidimensional feature space on to a number of
  parallel axes
• Each feature is mapped one axis
• as many axes as need can be lined up side to side
• there is no limit to the number of dimensions
  that can be represented

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Examples Parallel Coordinates - 1

• A single polygonal line connects the individual
  coordinate mappings for each point
• The technique has been applied in air traffic
  control, robotics, computer vision and
  computational geometry

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Examples Parallel Coordinates - 2
Ci
Ci-1
Ci-1
Cn
C1
X1 X2 X3 Xi-1
Xn

• Parallel axes for RN. The polygonal line shown
  represents the point C (C1, .... , C i-1, Ci,
  Ci1, ... , Cn)

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Examples Parallel Coordinates - 3

• The Parallel Coordinates visualization technique
  is employed in the software WinViz
  http//www.computer.org/intelligent/ex1996/x5069ab
  s.htm
• The main advantage of the technique is that it
  can represent unlimited numbers of dimensions

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Examples Parallel Coordinates - 3

• When many points are represented using the
  parallel coordinates, the overlap of the
  polygonal lines can make it difficult to identify
  structures in the data.
• Certain structures, such as clusters, can often
  be identified but others are hidden due to the
  overlap.

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Two Clusters In WinViz
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Examples Stick Figures

• The stick figure technique is intended to make
  use of the users low-level perceptual processes
  PGL1995, such as perception of
• texture, color, motion, and depth
• The hope is that the user will automatically
  try to make physical sense of the pictures of the
  data created

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Examples Stick Figures

• Visualizations which represent multidimensional
  feature spaces by using a number of subspaces of
  3D or less (e.g. scatterplots) rely more on our
  cognitive abilities than our perceptual abilities
• Stick figures avoid this, and present all
  variables and data points in a single
  representation.

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Iconographic display using stick figures - US
Census Datahttp//ivpr.cs.uml.edu/gallery/
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Examples Pixel-based techniqueshttp//www.dbs.in
formatik.uni-muenchen.de/dbs/projekt/visdb/visdb.h
tml

• Query-Dependent Pixel-based Techniques
• based on a query, a semantic distance is
  calculated between each of the query feature
  values and the features of each instance in the
  DB
• Distance is mapped to colour for each attribute
• Overall distance between the data values for a
  specific instance and the data attribute values
  used in the predicate of the query is also
  calculated

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Examples Pixel-based techniqueshttp//www.dbs.in
formatik.uni-muenchen.de/dbs/projekt/visdb/visdb.h
tml

• Instances are arranged on the screen, with the
  data items with highest relevance in the centre
  of the display, and then proceeding outwards in a
  spiral
• the values for each of the attributes are
  presented in separate subwindows
• the arrangement inside the subwindows is
  according to the overall distance

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Query-Dependent Pixel-based Techniques
Overall Distance

• Result of a complex query KeK1994

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Examples Worlds within Worldshttp//www.cs.colum
bia.edu/graphics/projects/AutoVisual/AutoVisual.ht
ml

• Employs virtual reality devices to represent an
  nD virtual world in 3D or 4D-Hyperworlds
• basic approach to reducing the complexity of a
  multidimensional function is to hold one or more
  of its independent variables constant
• equivalent to taking an infinitely thin slice of
  the world perpendicular to the constant
  variables axis
• can be repeated until there are 3 dimensions and
  the resulting slice can be manipulated and
  displayed with conventional 3D graphics hardware

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Examples Worlds within Worldshttp//www.cs.colum
bia.edu/graphics/projects/AutoVisual/AutoVisual.ht
ml

• After reducing the higher-dimensional space to 3
  dimensions the additional dimensions can be added
  back, by adding additional 3D worlds within the
  first 3D world

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Worlds within Worlds
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Dynamic Techniques

• Allow interaction with the visualization to
  explore the data more effectively. Can
  potentially be applied to all visualization
  techniques
• Dynamic linking of the data attributes to the
  parameters of the visualization.
• Filtering
• Linking and brushing between multiple
  visualizations
• Zooming
• Details on demand

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Other Techniques

• Keim and Kriegels query independent approach
• Chernoff faceshttp//www.fas.harvard.edu/stats/C
  hernoff/Hcindex.htm
• Cone trees
• Perspective walls
• Visualization Spreadsheet
• A number of techniques especially developed for
  web pages and their links

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Web References

• More lectures and demo software available at
• http//www.cs.auc.dk/DVDM/courses.html