Thank you for coming here!

Purpose of Experiment Compare two visualization

systems. You will play with one of them.

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.

Schedule First, I will present ...Multidimensional dataHierarchical Parallel CoordinatesBrushingFeature findingIntroduce the visualization system

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.

Outline Multidimensional Data How to represent multidimensional

data Parallel Coordinates Hierarchical Clustering Hierarchical Parallel Coordinates

Brushing Operation Feature Finding

Multidimensional DataExample: Iris Data

Scientists measured the sepal length, sepal width, petal length, petal width of many kinds of iris...

Multidimensional DataExample: Iris Data

sepallength

sepalwidth

petallength

petalwidth

5.1 3.5 1.4 0.24.9 3 1.4 0.2... ... ... ...5.9 3 5.1 1.8

Outline Multidimensional Data How to represent multidimensional

data Parallel Coordinates Hierarchical Clustering Hierarchical Parallel Coordinates

Brushing Operation Feature Finding

Parallel Coordinates One-Dimensional Data

1 2(1.6)

Parallel Coordinates 4-Dimensional Iris Data Set

sepallength

sepalwidth

petallength

petalwidth

5.1 3.5 1.4 0.24.9 3 1.4 0.2... ... ... ...5.9 3 5.1 1.8

5.1

3.5

sepallength

sepalwidth

petallength

petalwidth

5.1 3.5 1.4 0.2

1.4 0.2

Outline Multidimensional Data How to represent multidimensional

data Parallel Coordinates Hierarchical Clustering Hierarchical Parallel Coordinates

Brushing Operation Feature Finding

Hierarchical ClusteringHierarchical Cluster Tree

A cluster tree

Hierarchical ClusteringMean, Extent

P2

P1

C1

P1( 3, 6) p2( 5, 5)

Mean Point of C1 = (P1+P2)/2 = (4, 5.5)

Extent of C1:x:[3, 5] y:[5, 6]

x

y

Outline Multidimensional Data How to represent multidimensional

data Parallel Coordinates Hierarchical Clustering Hierarchical Parallel Coordinates

Brushing Operation Feature Finding

Outline Multidimensional Data How to represent multidimensional

data Parallel Coordinates Hierarchical Clustering Hierarchical Parallel Coordinates

Brushing Operation Feature Finding

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

Outline Multidimensional Data How to represent multidimensional

data Parallel Coordinates Hierarchical Clustering Hierarchical Parallel Coordinates

Brushing Operation Feature Finding

Feature FindingFeature - 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.

Other featuresYou can record anything else you find with the help of the visualization system.