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Visualizing Multi-dimensional Clusters, Trends, and Outliers using Star Coordinates

Visualizing Multi-dimensional Clusters, Trends, and Outliers using Star Coordinates. Author : Eser Kandogan Reporter : Tze Ho-Lin 2007/5/9. SIGKDD, 2001. Outline. Motivation Objectives Methodology: Star Coordinates Interaction techniques Evaluation Conclusion Personal Comments.

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Visualizing Multi-dimensional Clusters, Trends, and Outliers using Star Coordinates

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  1. Visualizing Multi-dimensional Clusters, Trends, and Outliers using Star Coordinates Author : Eser Kandogan Reporter : Tze Ho-Lin 2007/5/9 SIGKDD, 2001

  2. Outline • Motivation • Objectives • Methodology: Star Coordinates • Interaction techniques • Evaluation • Conclusion • Personal Comments

  3. Motivation • Real datasets contain typically more than three attributes of data, representing and making sense of multi-dimensional data has been challenging.

  4. Objectives • The objective for this paper is to relieve the dimensionality curse on knowledge discovery through simple data representations that are derived from familiar and easy to understand lower dimensional representations.

  5. Methodology

  6. Methodology j: 資料點 i: 屬性

  7. Interaction techniques • Scaling • Rotation • Marking • Range Selection • Histogram • Footprints • Sticks

  8. Evaluation

  9. Conclusion • Star Coordinates, aims to let a representation of the higher dimensional space built on the well-known simple representations and also through dynamic interactions that allow users to discover trends, outliers, and clusters easily.

  10. Personal Comments • Application • Data visualization • Advantage • Simple & Easy to understand • Disadvantage • The figures in this paper is rough.

  11. Scaling

  12. Rotation

  13. Range Selection

  14. Histogram

  15. Footprints

  16. Sticks

  17. Evaluation - Figure 12

  18. Evaluation - Figure 13

  19. Evaluation - Figure 14 Figure 14. Data point distribution after removing state, area code, phone number, and total minute and calls for day, evening, night, and international calls.

  20. Evaluation - Figure 15 Figure 15. Data point partitioned into four clusters based on international service plan and voice plan membership.

  21. Evaluation - Figure 16 Figure 16. Total day charge and number of customer service calls play the most significant role in churn for customers without international and voice mail service plans‧.

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