An Excel-based Data Mining Tool

Chapter 4

4.1 The iData Analyzer

Figure 4.1 The iDA system architecture

Data

PreProcessor

Interface

HeuristicAgent

NeuralNetworks

LargeDataset

ESX

MiningTechnique

GenerateRules

RulesRuleMaker

ReportGenerator

ExcelSheets

Explaination

Yes

No

No

Yes

Yes

No

Figure 4.2 A successful installation

4.2 ESX: A Multipurpose Tool for Data Mining

Figure 4.3 An ESX concept hierarchy

Root

CnC1 C2

I11 I1jI12

Root Level

Instance Level

Concept Level

. . .

. . .

I21 I2kI22

. . . In1 InlIn2

. . .

4.3 iDAV Format for Data Mining

Table 4.1 • Credit Card Promotion Database: iDAV Format

Income Magazine Watch Life Insurance Credit CardRange Promotion Promotion Promotion Insurance Sex Age

C C C C C C RI I I I I I I

40–50K Yes No No No Male 4530–40K Yes Yes Yes No Female 4040–50K No No No No Male 4230–40K Yes Yes Yes Yes Male 4350–60K Yes No Yes No Female 3820–30K No No No No Female 5530–40K Yes No Yes Yes Male 3520–30K No Yes No No Male 2730–40K Yes No No No Male 4330–40K Yes Yes Yes No Female 4140–50K No Yes Yes No Female 4320–30K No Yes Yes No Male 2950–60K Yes Yes Yes No Female 3940–50K No Yes No No Male 5520–30K No No Yes Yes Female 19

Table 4.2 • Values for Attribute Usage

Character Usage

I The attribute is used as an input attribute.

U The attribute is not used. D The attribute is not used for classification or clustering, but

attribute value summary information is displayed in all output reports.

O The attribute is used as an output attribute. For supervised learning with ESX, exactly one categorical attribute is selected as the output attribute.

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

Summary Results

• Class Resemblance Score offers a first indication about how well the instances within each class (cluster) fit together.

• Domain Resemblance Score represents the overall similarity of all instances within the data set.

• It is highly desirable that class resemblance scores are higher that the domain resemblance score

Summary Results

• Given categorical attribute A with values v1, v2, v3, …, vi,… vn, the Domain Predictability of vi tells us the domain instances showing vi as a value for A.

• A predictability score near 100% for a domain-level categorical attribute value indicates that the attribute is not likely to be useful for supervised learning or unsupervised clustering

Summary Results• Given categorical attribute A with values v1, v2, v3, …, vi,… vn, the Class C Predictability score for vi tells us the percent of instances within class C shoving vi as a value for A.

• Given class C and categorical attribute A with values v1, v2, v3, …, vi,… vn, an Attribute-Value Predictiveness score for vi is defined as the probability an instance resides in C given the instance has value vi for A.

Domain Statistics for Numerical Attributes

• Attribute Significance Value measures the predictive value of each numerical attribute.

• To calculate the Attribute Significance Value for a numeric attribute, it is necessary to: a) subtract the smallest class mean from the largest mean value; b) divide this result by the domain standard deviation

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.

Necessary and Sufficient Attribute Values

• If an attribute value has a predictability and predictiveness score of 1.0, the attribute value is said to be necessary and sufficient for membership in class C. That is, all instances within class C have the specified value for the attribute and all instances with this value for the attribute reside in class C.

Sufficient Attribute Values

• If an attribute value has a predictiveness score of 1.0 and a predictability score less than 1.0, the attribute value is said to be sufficient but not necessary for membership in class C. That is, all instances with the value for the attribute reside in C, but there are other instances in C that have a different value for this attribute.

Necessary Attribute Values

• If an attribute value has a predictability score of 1.0 and a predictiveness score less than 1.0, the attribute value is said to be necessary but not sufficient for membership in class C. That is, all instances in C have the same value for the attribute, but there are other instances outside C, have the same value for this attribute.

Necessary and Sufficient Attribute Values in iDA

• The attribute values with predictiveness scores greater than or equal to 0.8 are considered as highly sufficient.

• The attribute values with predictability scores greater than or equal to 0.8 are considered as necessary.

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

Rule Interpretation in iDA

• Each rule simply declares the precondition(s) necessary for an instance to be covered by the rule:

• if [(condition & condition &…& condition)=true] then an instance resides in a certain class.

Rule Interpretation in iDA

• Rule accuracy tells us the rule is accurate in …% of all cases where it applies.

• Rule coverage shows that the rule applies that the rule applies to …% of class instances

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

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

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