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PracticalDataMining
COMP-321B
Tutorial6:AssociationRules
GabiSchmidberger
MarkHall
September11,2006
c2006UniversityofWaikato
1Introduction
oneassociationruleslearnerisused:Apriori(weka.associations.Apriori).
1.1Introductiontothedatasetsused
rules.2TheAssociatePanel
onpreciseevaluation. 1 ===Runinformation===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 part.Butitshouldlooklikethis: ===Associatormodel(fulltrainingset)===Apriori
Minimumsupport:0.45(196instances)
Minimummetric:0.9
Numberofcyclesperformed:11
Generatedsetsoflargeitemsets:
SizeofsetoflargeitemsetsL(1):20
SizeofsetoflargeitemsetsL(2):17
SizeofsetoflargeitemsetsL(3):6
SizeofsetoflargeitemsetsL(4):1
2 hasacertainsupportandcondencevalue.3UsingApriori
Ifnotyetloaded,loadthedataset`vote.ar'.
Writedowntheproportionasdivision.
bersonthePreprocesspanel.) plainusingrulenumber9asexample. that? thebestrulesare? contras? democraticcongressmen? 34Theweatherdatasetagain
alreadyusedinthersttutorial.TaskB1:Considertherule:
temperature=hot==>humidity=normal.Preprocesspaneltoanswerthisquestion.)
TaskB2:Considertherule:
instancesthatapplytothisrule. sidelikeintheexamplebelow: outlook=sunnytemperature=cool ==>humidity=normalplay=yes5Makeassociationrulesforthesupermarket
datasetLoadthedataset`supermarket.ar'.
sideoftherules. downtherelevantrulesforthisobservation. forthisobservation. 4 be?6Answers
AnswerA3:
AnswerA4:
AnswerA5:
AnswerA6:
AnswerA7:
AnswerA8:
5AnswerB1:
temperature=hot==>humidity=normal.AnswerB2:
AnswerB3:
6AnswerC1:
AnswerC2:
AnswerC3:
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