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Below is information I have compiled using a data mining program called Weka, using the Apriori a


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Below is information I have compiled using a data mining program called Weka, using the Apriori algorithm. What I need help with is an explanation of each of the 10 rules, and what does the confidence of each rule mean. Here are the 10 rules. 


1. adoption-of-the-budget-resolution=y physician-fee-freeze=n 219 ==> Class=democrat 219 conf:(1)

2. adoption-of-the-budget-resolution=y physician-fee-freeze=n aid-to-nicaraguan-contras=y 198 ==> Class=democrat 198 conf:(1)

3. physician-fee-freeze=n aid-to-nicaraguan-contras=y 211 ==> Class=democrat 210 conf:(1)

4. physician-fee-freeze=n education-spending=n 202 ==> Class=democrat 201 conf:(1)

5. physician-fee-freeze=n 247 ==> Class=democrat 245 conf:(0.99)

6. el-salvador-aid=n Class=democrat 200 ==> aid-to-nicaraguan-contras=y 197 conf:(0.99)

7. el-salvador-aid=n 208 ==> aid-to-nicaraguan-contras=y 204 conf:(0.98)

8. adoption-of-the-budget-resolution=y aid-to-nicaraguan-contras=y Class=democrat 203 ==> physician-fee-freeze=n 198 conf:(0.98)

9. el-salvador-aid=n aid-to-nicaraguan-contras=y 204 ==> Class=democrat 197 conf:(0.97)

10. aid-to-nicaraguan-contras=y Class=democrat 218 ==> physician-fee-freeze=n 210 conf:(0.96)


The data in its entirety is here........

=== Run information ===


Scheme: weka.associations.Apriori -N 10 -T 0 -C 0.9 -D 0.05 -U 1.0 -M 0.1 -S -1.0 -c -1

Relation: vote

Instances: 435

Attributes: 17

handicapped-infants

water-project-cost-sharing

adoption-of-the-budget-resolution

physician-fee-freeze

el-salvador-aid

religious-groups-in-schools

anti-satellite-test-ban

aid-to-nicaraguan-contras

mx-missile

immigration

synfuels-corporation-cutback

education-spending

superfund-right-to-sue

crime

duty-free-exports

export-administration-act-south-africa

Class

=== Associator model (full training set) ===



Apriori

=======


Minimum support: 0.45 (196 instances)

Minimum metric : 0.9

Number of cycles performed: 11


Generated sets of large itemsets:


Size of set of large itemsets L(1): 20


Size of set of large itemsets L(2): 17


Size of set of large itemsets L(3): 6


Size of set of large itemsets L(4): 1


Best rules found:


1. adoption-of-the-budget-resolution=y physician-fee-freeze=n 219 ==> Class=democrat 219 conf:(1)

2. adoption-of-the-budget-resolution=y physician-fee-freeze=n aid-to-nicaraguan-contras=y 198 ==> Class=democrat 198 conf:(1)

3. physician-fee-freeze=n aid-to-nicaraguan-contras=y 211 ==> Class=democrat 210 conf:(1)

4. physician-fee-freeze=n education-spending=n 202 ==> Class=democrat 201 conf:(1)

5. physician-fee-freeze=n 247 ==> Class=democrat 245 conf:(0.99)

6. el-salvador-aid=n Class=democrat 200 ==> aid-to-nicaraguan-contras=y 197 conf:(0.99)

7. el-salvador-aid=n 208 ==> aid-to-nicaraguan-contras=y 204 conf:(0.98)

8. adoption-of-the-budget-resolution=y aid-to-nicaraguan-contras=y Class=democrat 203 ==> physician-fee-freeze=n 198 conf:(0.98)

9. el-salvador-aid=n aid-to-nicaraguan-contras=y 204 ==> Class=democrat 197 conf:(0.97)

10. aid-to-nicaraguan-contras=y Class=democrat 218 ==> physician-fee-freeze=n 210 conf:(0.96)


RUNNING HEAD: Mining Association Rules with Weka Mining Association Rules with Weka

 

MIS 450 Data Mining

 

Colorado State University ? Global Campus

 

Dr. Annie Shebanow

 

January 3, 2016 1 Mining Association Rules with Weka

 

Mining Association Rules with Weka

 

Screenshot of Weka Explore when ?vote.arff? is successfully loaded 2 Mining Association Rules with Weka 3 Classification Complete Screenshot Explaining The Rules

 

Looking at the data in the ?vote.arff? file, it would seem this data is relevant to the

 

operations of an international aid organization. Below is an explanation of the rules generated for

 

this exercise and a brief explanation of the confidence of each rule. ?Support determines how

 

often a rule is applicable to a given data set. While confidence determines how frequently items

 

in Y appear in transactions that contain X? (Tan, Steinbach, Kumar, 2005, p. 329 - 330). Mining Association Rules with Weka 4 1. adoption-of-the-budget-resolution=y physician-fee-freeze=n 219 ==> Class=democrat 219

 

conf:(1)..

 

This rule examines the amount of votes Democrats will contribute to voting on the issues of

 

adopting the budget resolution and the physician fee freeze. 219 Democrats will vote yes for

 

these proposals. This rule has 100% Confidence.

 

2. adoption-of-the-budget-resolution=y physician-fee-freeze=n aid-to-Nicaraguan-contras=y 198

 

==> Class=democrat 198 conf:(1) This rule also has 100% confidence which means it is accurate. This rule asserts that if the

 

adoption of the budget resolution, along with the physician fee freeze, and aid to the Nicaraguan

 

contras is included, this would be approved by and voted on by 198 democrats. Going from the

 

first rule to the second rule, one can assert that the aid the Nicaraguan Contras may cost support

 

for the overall budget resolution.

 

3. physician-fee-freeze=n aid-to-Nicaraguan-contras=y 211 ==> Class=democrat 210 conf:(1) This rule does not mention the budget resolution but only mentions the physician fee freeze and

 

aid to the Nicaraguan Contras. This rule, which has 100% confidence asserts that if the physician

 

fee freeze and aid to Nicaraguan Contras is voted on collectively, 210 Democrats will vote for

 

this. Mining Association Rules with Weka 5 4. physician-fee-freeze=n education-spending=n 202 ==> Class=democrat 201 conf:(1) By now it has been determined that this data is in reference to the amount of Democrats voting

 

on various things and, if and how they would vote, based on how certain issues are grouped

 

together. This rule addresses the physician fee freeze and education spending. This rule is telling

 

me that 202 Democrats will vote on these issues collectively. Or out of 202 Democrats 201 voted

 

for this.

 

5. physician-fee-freeze=n 247 ==> Class=democrat 245 conf:(0.99) This rule simply addresses the physician fee freeze. And that out of 247 Democrats, 245 voted

 

for this. This rule has 99% confidence.

 

6. El-Salvador-aid=n Class=democrat 200 ==> aid-to-Nicaraguan-contras=y 197 conf:(0.99) This rule states that?s 200 Democrats want aid to go to El Salvador, and 197 want aid to go to the

 

Nicaraguan Contras.

 

7. El-Salvador-aid=n 208 ==> aid-to-Nicaraguan-contras=y 204 conf:(0.98) This rule states that 208 are in favor or aid going to El Salvador, and 204 are in favor of aid

 

going to the Nicaraguan Contras, with a confidence of 98%. Mining Association Rules with Weka 6 8. adoption-of-the-budget-resolution=y aid-to-Nicaraguan-contras=y Class=democrat 203 ==>

 

physician-fee-freeze=n 198 conf:(0.98) This rule asserts that the adoption of the budget resolution, along with aid to the Nicaraguan

 

Contras will be approved by 203 Democrats. If the Physician fee freeze is included, this vote

 

count goes to 198. The confidence level for this rule is 98%.

 

9. El-Salvador-aid=n aid-to-Nicaraguan-contras=y 204 ==> Class=democrat 197 conf:(0.97) This rule asserts that 204 Democrats are in favor of the El Salvador Aid and aid going to the

 

Nicaraguan Contras. Of these 204 votes 197 are Democrat. This rule has a confidence of 97%.

 

10. aid-to-Nicaraguan-contras=y Class=democrat 218 ==> physician-fee-freeze=n 210 conf: (0.96)

 

This rule asserts that aid to the Nicaraguan Contras 218 Democrats are in favor of, and 210 votes

 

in favor of the physician fee freeze. This rule has a confidence of 96%. Mining Association Rules with Weka 7 References

 

Tan, P., Steinbach, M., & Kumar, V. (2005). Introduction to data mining (1st ed.). Boston,

 

Massachusetts: Pearson Addison Wesley.

 


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[solution] »

Below is information I have compiled using a data mining program called Weka, using the Apriori a.zip

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This paper was answered on 14-Oct-2020

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