visualization and data mining daniel a. keim professor and head of data mining and information...

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Visualization and Data Mining Daniel A. Keim Professor and Head of Data Mining and Information Visualization University of Constance 78457 Konstanz, Germany [email protected]

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Visualization and Data Mining

Daniel A. Keim

Professor and Head of Data Mining and Information Visualization

University of Constance

78457 Konstanz, Germany

[email protected]

May 2, 2005 2

Comments

• Tight Integration of Data Mining and Visualization Automatic Data Mining for Data Preprocessing

Using Visualization to Steer Automatic Data Analysis

Automatic Analysis Techniques for selecting the visualization

• Important Challenges

- Business Analytics: CRM, Marketing, Finance, …

- Network Analytics: Monitoring, Security, … many involve GIS

differences to NVAC ?

My Name, title and AffiliationDaniel Keim, University of Constance

May 2, 2005 3

Tight Integration

Data

Knowledge

DM-Algorithm

Result

Visualization ofthe data

PrecedingVisualization

Visualization of the result

Data

DM-Algorithm

Knowledge

Visualization of the result

Result

SubsequentVisualization

Result

DM-Algorithmstep 1

Data

Knowledge

DM-Algorithmstep n

Visualization

+ Inte

raction

Tightly integratedVisualization

May 2, 2005 4

Tightly Integrated Visualization

Result

DM-Algorithmstep 1

Data

Knowledge

DM-Algorithmstep n

Visualization

+ Inte

raction

Visualization of algorithmic decisions

Data and patterns are better understood

User can make decisions based on perception

User can make decisions based on domain knowledge

Visualization of result enables user specified feedback for next algorithmic run

May 2, 2005 5

Visual Classification [AEK 00]

Each attribute is sorted and visualized separately

Each attribute value is mapped onto a unique pixel

The color of a pixel is determined by the class label of the object

The order is reflected by the arrangement of the pixels

...

attr. 1 class

0.2 Y

0.3 Y

0.3 Y

0.5 N

1.1 Y

... ...

attr. 2 class

0.5 N

1.3 Y

2.0 N

2.5 Y

5.1 N

... ...

attr.1 attr.2 ... class

0.3 23.3 ... Y

2.4 2.0 ... N

... ... ... ...

May 2, 2005 6

Visual Classification

age < 35

G PV

SalaryG

< 40 > 80[40,80]

A New Visualization of a Decision Tree

May 2, 2005 7

Example of Tight Integration:Visual Classification

Level 1Level 2

...

Level 18

leaf

split point

inherited split point

Geo-related information: Learning from History

May 2, 2005 9

Computer generated Cartograms

PresidentialElection 2000Results

Bush – Gore

May 2, 2005 10

Rectangular Cartograms: 2004 Election Results