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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 keim@informatik.uni-konstanz.de. Comments. Tight Integration of Data Mining and Visualization

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Visualization and Data Mining

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  1. Visualization and Data Mining Daniel A. Keim Professor and Head of Data Mining and Information Visualization University of Constance 78457 Konstanz, Germany keim@informatik.uni-konstanz.de

  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

  3. 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

  4. 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

  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

  6. Visual Classification • A New Visualization of a Decision Tree age < 35 G Salary < 40 > 80 [40,80] P V G

  7. Example of Tight Integration:Visual Classification Level 1 Level 2 ... leaf split point inherited split point Level 18

  8. Geo-related information: Learning from History

  9. Computer generated Cartograms Presidential Election 2000 Results Bush – Gore

  10. Rectangular Cartograms: 2004 Election Results

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