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. Class Predictiveness is a 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

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

4.6 Techniques for Generating Rules Simple Procedure for Creating Best Set of Covering Rules 1.Choose an attribute that best differentiate all domains. 2.Use the attribute to further subdivide instances into classes. 3.For each subclass created in step If the instances in the subclass satisfy a predefined criteria Then generate a defining rule for the subclass. 3.2 If the subclass does not satisfy the predefined criteria Then repeat step 1

4.6 Techniques for Generating Rules (RuleMaker) 1.Define the scope of the rules. 2.Choose the instances. 3.Set the minimum rule correctness. 4.Define the minimum rule coverage. 5.Choose an attribute significance value.

4.7 Instance Typicality The average similarity of instance to all other instances within its class. 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 – at some point copy the original data into another Excel sheet The Quick Mine Feature – recommended when the dataset contains more than 2000 instances Erroneous and Missing Data – blank lines, beyond the last column, invalid characters