An Excel-based Data Mining Tool Chapter 4

4.1 The iData Analyzer

Figure 4.1 The iDA system architecture

Figure 4.2 A successful installation

4.2 ESX: A Multipurpose Tool for Data Mining

Figure 4.3 An ESX concept hierarchy

4.3 iDAV Format for Data Mining

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

Step 1: Enter The Data To Be Mined

Figure 4.4 The Credit Card Promotion Database

Step 2: Perform A Data Mining Session

Figure 4.5 Unsupervised settings for ESX

Figure 4.6 RuleMaker options

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

Figure 4.8 Summery statistics for the Acme credit card promotion database

Figure 4.9 Statistics for numerical attributes and common categorical attribute values

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)

between-class measure.

Figure 4.10 Class 3 summary results

Figure 4.11 Necessary and sufficient attribute values for Class 3

Step 5: Visualize Individual Class Rules

Figure 4.7 Rules for the credit card promotion database

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

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

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

4.7 Instance Typicality

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

Figure 4.13 Instance typicality

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