Thank you for coming here!

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Thank you for coming here!
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Purpose of Experiment

• Compare two visualization systems.
• You will play with one of them.

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What will you do?

• Learn a multidimensional visualization system
• Use it to find features of a data set and record
  your result
• A quick after-experiment feedback.

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Schedule

• First, I will present ...
• Multidimensional data
• Hierarchical Parallel Coordinates
• Brushing
• Feature finding
• Introduce the visualization system

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Schedule

• Then, You will do ...
• Experiment
• -Find features of a given data set using
  the visualization system
• -Record the features you find
• Fill feedback form.

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Outline

• Multidimensional Data
• How to represent multidimensional data
• Parallel Coordinates
• Hierarchical Clustering
• Hierarchical Parallel Coordinates
• Brushing Operation
• Feature Finding

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Multidimensional DataExample Iris Data
Scientists measured the sepal length, sepal
width, petal length, petal width of many kinds
of iris...
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Multidimensional DataExample Iris Data
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Outline

• Multidimensional Data
• How to represent multidimensional data
• Parallel Coordinates
• Hierarchical Clustering
• Hierarchical Parallel Coordinates
• Brushing Operation
• Feature Finding

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Parallel Coordinates One-Dimensional Data
(1.6)
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Parallel Coordinates 4-Dimensional Iris Data Set
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3.5
5.1
1.4
0.2
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Outline

• Multidimensional Data
• How to represent multidimensional data
• Parallel Coordinates
• Hierarchical Clustering
• Hierarchical Parallel Coordinates
• Brushing Operation
• Feature Finding

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Hierarchical ClusteringHierarchical Cluster Tree
A cluster tree
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Hierarchical ClusteringMean, Extent
y
P1( 3, 6) p2( 5, 5) Mean Point of C1
(P1P2)/2 (4, 5.5) Extent of C1 x3, 5
y5, 6
P1
P2
C1
x
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Outline

• Multidimensional Data
• How to represent multidimensional data
• Parallel Coordinates
• Hierarchical Clustering
• Hierarchical Parallel Coordinates
• Brushing Operation
• Feature Finding

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Outline

• Multidimensional Data
• How to represent multidimensional data
• Parallel Coordinates
• Hierarchical Clustering
• Hierarchical Parallel Coordinates
• Brushing Operation
• Feature Finding

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Brushing
Brushing - Highlighting part of the clusters to
distinguish them from the other clusters.
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Outline

• Multidimensional Data
• How to represent multidimensional data
• Parallel Coordinates
• Hierarchical Clustering
• Hierarchical Parallel Coordinates
• Brushing Operation
• Feature Finding

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Feature Finding
Feature - Anything you find from the data set.
Cluster - A group of data items that are similar
in all dimensions. Outlier - A data item that is
similar to FEW or No other data items.
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Other features
You can record anything else you find with the
help of the visualization system.