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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] Comments. Tight Integration of Data Mining and Visualization

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visualization and data mining

Visualization and Data Mining

Daniel A. Keim

Professor and Head of Data Mining and Information Visualization

University of Constance

78457 Konstanz, Germany

[email protected]

comments
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

tight integration
Data

Data

Data

DM-Algorithm

step 1

Visualization of

the data

Visualization of

DM-Algorithm

the result

DM-Algorithm

step n

Visualization + Interaction

DM-Algorithm

Result

Result

Visualization of

the result

Result

Knowledge

Knowledge

Knowledge

Subsequent

Visualization

Tightly integrated

Visualization

Preceding

Visualization

Tight Integration
tightly integrated visualization
Tightly Integrated Visualization
  • 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

Data

DM-Algorithm

step 1

DM-Algorithm

step n

Visualization + Interaction

Result

Knowledge

visual classification aek 00
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
visual classification
Visual Classification
  • A New Visualization of a Decision Tree

age < 35

G

Salary

< 40

> 80

[40,80]

P

V

G

example of tight integration visual classification
Example of Tight Integration:Visual Classification

Level 1

Level 2

...

leaf

split point

inherited split point

Level 18

slide9
Computer generated Cartograms

Presidential

Election 2000

Results

Bush – Gore

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