Dwdm exp6

 6. Demonstrate knowledge flow application on data sets

age	specticle_prescrip	astigmation	tear_prod_rate	contact_lenses
young	myopia	no	reduce	none
young	myopia	no	normal	soft
young	myopia	yes	reduce	none
young	myopia	yes	normal	hard
young	hypermertropia	no	reduce	none
young	hypermertropia	no	normal	soft
young	hypermertropia	yes	reduce	none
young	hypermertropia	yes	normal	hard
pre_pres_byopic	myopia	no	reduce	none
pre_pres_byopic	myopia	no	normal	none
pre_pres_byopic	myopia	yes	reduce	soft
pre_pres_byopic	myopia	yes	normal	hard
pre_pres_byopic	hypermertropia	yes	normal	none
pre_pres_byopic	hypermertropia	no	reduce	normal
pre_pres_byopic	hypermertropia	yes	reduce	normal
pre_pres_byopic	myopia	no	normal	none
pre_pres_byopic	myopia	yes	reduce	none
pres_byopic	hypermertropia	yes	normal	soft

1.Develop a knowledge flow layout for finding strong association rules by using, Apriori algorithm

Aim: Demonstration of association rule process on contactlens.arff using apriori algorithm.

 @relation contactlenses

@attribute age {young,pre_pres_byopic,pres_byopic}

@attribute specticle_prescrip {myopia,hypermertropia}

@attribute astigmation {yes,no}

@attribute tear_prod_rate {reduce,normal}

@attribute contact_lenses {none,soft,hard,normal}

@data

young,myopia,no,reduce,none

young,myopia,no,normal,soft

young,myopia,yes,reduce,none

young,myopia,yes,normal,hard

young,hypermertropia,no,reduce,none

young,hypermertropia,no,normal,soft

young,hypermertropia,yes,reduce,none

young,hypermertropia,yes,normal,hard

pre_pres_byopic,myopia,no,reduce,none

pre_pres_byopic,myopia,no,normal,none

pre_pres_byopic,myopia,yes,reduce,soft

pre_pres_byopic,myopia,yes,normal,hard

pre_pres_byopic,hypermertropia,yes,normal,none

pre_pres_byopic,hypermertropia,no,reduce,normal

pre_pres_byopic,hypermertropia,yes,reduce,normal

pre_pres_byopic,myopia,no,normal,none

pre_pres_byopic,myopia,yes,reduce,none

pres_byopic,hypermertropia,yes,normal,soft

Step1:open the data file in weka explorer it is presume the required data have described .

Step2: clicking an association up will bring up the Interface for association algorithm.

Step3:we use Apriori algorithm which is default algorithm 

Step 4: In order to change the parameter click on the text box immediately to the right of the choose button.

Step 5: : Now we click on start to rum the Apriori algorithm.

output: 

=== 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:     contactlenses

Instances:    18

Attributes:   5

              age

              specticle_prescrip

              astigmation

              tear_prod_rate

              contact_lenses

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

Apriori

=======

Minimum support: 0.15 (3 instances)

Minimum metric <confidence>: 0.9

Number of cycles performed: 17

Generated sets of large itemsets:

Size of set of large itemsets L(1): 11

Size of set of large itemsets L(2): 35

Size of set of large itemsets L(3): 17

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

Best rules found:

 1. age=young contact_lenses=none 4 ==> tear_prod_rate=reduce 4    conf:(1)

 2. age=young tear_prod_rate=reduce 4 ==> contact_lenses=none 4    conf:(1)

 3. contact_lenses=hard 3 ==> astigmation=yes 3    conf:(1)

 4. contact_lenses=hard 3 ==> tear_prod_rate=normal 3    conf:(1)

 5. tear_prod_rate=normal contact_lenses=none 3 ==> age=pre_pres_byopic 3    conf:(1)

 6. tear_prod_rate=normal contact_lenses=hard 3 ==> astigmation=yes 3    conf:(1)

 7. astigmation=yes contact_lenses=hard 3 ==> tear_prod_rate=normal 3    conf:(1)

 8. contact_lenses=hard 3 ==> astigmation=yes tear_prod_rate=normal 3    conf:(1)

 9. age=pre_pres_byopic astigmation=no contact_lenses=none 3 ==> specticle_prescrip=myopia 3    conf:(1)

10. age=pre_pres_byopic specticle_prescrip=myopia astigmation=no 3 ==> contact_lenses=none 3    conf:(1)