1. An Excel-based Data Mining Tool
Chapter 4

2. 4.1 The iData Analyzer

3. Figure 4.1 The iDA system architecture

4. Figure 4.2 A successful installation

5. 4.2 ESX: A Multipurpose Tool for Data Mining

6. Figure 4.3 An ESX concept hierarchy

7. 4.3 iDAV Format for Data Mining

10. 4.4 A Five-step Approach for Unsupervised Clustering
Step 1: Enter the Data to be Mined
Step 2: Perform a Data Mining Session
Step 3: Read and Interpret Summary Results
Step 4: Read and Interpret Individual Class Results
Step 5: Visualize Individual Class Rules

11. Step 1: Enter The Data To Be Mined

12. Figure 4.4 The Credit Card Promotion Database

13. Step 2: Perform A Data Mining Session

14. Figure 4.5 Unsupervised settings for ESX

15. Figure 4.6 RuleMaker options

16. Step 3: Read and Interpret Summary Results
Class Resemblance Scores
Domain Resemblance Score
Domain Predictability

17. Figure 4.8 Summery statistics for the Acme credit card promotion database

18. Figure 4.9 Statistics for numerical attributes and common categorical attribute values

19. Step 4: Read and Interpret Individual Class Results
Class Predictability is a within-class measure. (=1 for necessary condition)
Class Predictiveness is a between-class measure. (=1 for sufficient condition)

20. between-class measure.

22. Figure 4.10 Class 3 summary results

23. Figure 4.11 Necessary and sufficient attribute values for Class 3

24. Step 5: Visualize Individual Class Rules

25. Figure 4.7 Rules for the credit card promotion database

26. 4.5 A Six-Step Approach for Supervised Learning
Step 1: Choose an Output Attribute
Step 2: Perform the Mining Session
Step 3: Read and Interpret Summary Results
Step 4: Read and Interpret Test Set Results
Step 5: Read and Interpret Class Results
Step 6: Visualize and Interpret Class Rules

27. Read and Interpret Test Set Results
Figure 4.12 Test set instance classification

28. 4.6 Techniques for Generating Rules
Ref. Figure 4.6
All rules or covering set rules
Define the scope of the rules.
Choose the instances.
Set the minimum rule correctness.
Define the minimum rule coverage.
Choose an attribute significance value

29. 4.7 Instance Typicality

30. Typicality Scores Identify prototypical and outlier instances.
Select a best set of training instances.
Used to compute individual instance classification confidence scores.

31. Figure 4.13 Instance typicality

32. 4.8 Special Considerations and Features
Avoid Mining Delays
The Quick Mine Feature
Erroneous and Missing Data