The document discusses clustering customer data using the k-means clustering algorithm in WEKA. It shows the results of running k-means clustering with different values of k (the number of clusters) on a customer transaction dataset. For each value of k, it displays the number of iterations, within-cluster sum of squared errors, and assignment of data points to clusters. The results are analyzed as the value of k is increased from 2 to 10 clusters.

1. C
WEKA
ฉัฐวีณ์ เหลืองมโนธรรม 54102011144
CP 463: Artificial Intelligence
1

2. การเก็บข้อมูล
2

3. การเก็บข้อมูล(ต่อ)
จากข้อมูลในสไลด์ที่แล้ว คือ ดังนี้
•แต่ละcolumn จะเป็นชื่อสินค้าที่ซื้อในแต่ละใบเสร็จ
•N=No
•Y=Yes
• Column brand คือ สาขาที่ซื้อสินค้า
• Column name คือ ชื่อของผู้ซื้อ
3

4. การเก็บข้อมูล(ต่อ)
• Column gender คือ เพศ
• Column weight คือ น้าหนัก
• Column sportsman คือ นักกีฬา
• Column glass คือ ใส่แว่น
4

5. ขั้นตอนการทางาน
โหลดข้อมูลเข้า
มา โดย
open file
ชื่อ file
as.csv และ
asd.csv
5

6. ขั้นตอนการทางาน(ต่อ)
เปลี่ยนไฟล์
.csv เป็น
.arff
6

7. Cluster
SimpleKMean
7

8. Cluster(ต่อ) numClusters = 2
numClusters =
2
8

9. Cluster(ต่อ) numClusters = 2
• Number of iterations: 4
• Within cluster sum of squared errors: 94.0
• Initial starting points (random):
• Cluster 0: Y,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N
• ,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,
• thin,TRUE,-0.034483,TRUE,FALSE
• Cluster 1: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N
• ,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,ram142,aye,female,
• thin,TRUE,-0.03
• Clustered Instances
• 0 13 ( 48%)
• 1 14 ( 52%)4483,TRUE,FALSE
9

10. Cluster(ต่อ) numClusters = 3
• Number of iterations: 3
• Within cluster sum of squared errors: 89.0
• Initial starting points (random):
• Cluster 0:
Y,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,
thin
• ,TRUE,-0.034483,TRUE,FALSE
• Cluster 1:
N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,ram142,aye,femal
e,thin
• ,TRUE,-0.034483,TRUE,FALSE
• Cluster 2:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,may,female,fa
t
• ,FALSE,0.034483,TRUE,TRUE
10

11. Cluster(ต่อ) numClusters = 4
• Number of iterations: 3
• Within cluster sum of squared errors: 83.0
• Initial starting points (random):
• Cluster 0:
Y,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,f
emale,thin,TRUE,-0.034483,TRUE,FALSE
• Cluster 1:
N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,ram142,aye,f
emale,thin,TRUE,-0.034483,TRUE,FALSE
• Cluster 2:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,may,fe
male,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 3:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,pttfashion,m
ay,female,fat,FALSE,0.034483,TRUE,TRUE
11

12. Cluster(ต่อ) numClusters = 5
• Number of iterations: 3
• Within cluster sum of squared errors: 61.0
• Initial starting points (random):
• Cluster 0:
Y,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 1:
N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 2:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,may,female,fat,FALSE,0.0344
83,TRUE,TRUE
• Cluster 3:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,pttfashion,may,female,fat,FALSE,
0.034483,TRUE,TRUE
• Cluster 4:
N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-
0.034483,TRUE,FALSE
12

13. Cluster(ต่อ) numClusters = 6
• Number of iterations: 2
• Within cluster sum of squared errors: 54.0
• Initial starting points (random):
• Cluster 0: Y,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 1: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 2:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,may,female,fat,FALSE,0.034483,TRUE,
TRUE
• Cluster 3:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,
TRUE,TRUE
• Cluster 4: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-
0.034483,TRUE,FALSE
• Cluster 5:
N,N,Y,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,T
RUE,TRUE
13

14. Cluster(ต่อ) numClusters = 7
• Number of iterations: 2
• Within cluster sum of squared errors: 52.0
• Initial starting points (random):
• Cluster 0: Y,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 1: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 2:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 3:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 4: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-
0.034483,TRUE,FALSE
• Cluster 5:
N,N,Y,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 6: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-
0.034483,TRUE,FALSE
14

15. Cluster(ต่อ) numClusters = 8
• Number of iterations: 2
• Within cluster sum of squared errors: 49.0
• Initial starting points (random):
• Cluster 0: Y,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 1: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 2:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 3:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 4: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-
0.034483,TRUE,FALSE
• Cluster 5:
N,N,Y,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 6: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-
0.034483,TRUE,FALSE
• Cluster 7: N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
15

16. Cluster(ต่อ) numClusters = 9
• Number of iterations: 2
• Within cluster sum of squared errors: 48.0
• Initial starting points (random):
• Cluster 0: Y,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 1: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 2:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 3:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 4: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-
0.034483,TRUE,FALSE
• Cluster 5:
N,N,Y,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 6: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-
0.034483,TRUE,FALSE
• Cluster 7: N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
16

17. Cluster(ต่อ) numClusters = 10
• Number of iterations: 2
• Within cluster sum of squared errors: 44.0
• Initial starting points (random):
• Cluster 0: Y,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 1: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
• Cluster 2:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 3:
N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 4: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-
0.034483,TRUE,FALSE
• Cluster 5:
N,N,Y,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 6: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-
0.034483,TRUE,FALSE
• Cluster 7: N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,thin,TRUE,-
0.034483,TRUE,FALSE
17

18. Cluster(ต่อ) numClusters = 11
• Number of iterations: 2
• Within cluster sum of squared errors: 40.0
• Initial starting points (random):
• Cluster 0: Y,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,thin,TRUE,-0.034483,TRUE,FALSE
• Cluster 1: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,ram142,aye,female,thin,TRUE,-0.034483,TRUE,FALSE
• Cluster 2: N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 3: N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 4: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-0.034483,TRUE,FALSE
• Cluster 5: N,N,Y,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,Y,N,N,Y,Y,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,TRUE,TRUE
• Cluster 6: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-0.034483,TRUE,FALSE
• Cluster 7: N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,ram142,aye,female,thin,TRUE,-0.034483,TRUE,FALSE
• Cluster 8: N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,N,swu,ton,male,thin,FALSE,-0.034483,TRUE,FALSE
• Cluster 9: N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,bangkae,ton,male,thin,FALSE,-0.034483,TRUE,FALSE
• Cluster 10: N,N,N,N,N,N,N,N,N,N,N,Y,N,N,N,N,N,N,N,N,N,N,N,N,N,N,Y,N,Y,N,Y,N,N,N,N,N,N,N,pttfashion,may,female,fat,FALSE,0.034483,TRUE,TRUE
18

19. 0
2
4
6
8
10
12
14
16
0 1 2 3 4 5 6 7 8 9 10
Knee Curve
C2 C3 C4 C5 C6 C7 C8 C9 C10
19

20. Classify
•แก้ไขไฟล์ก่อน โดยการลบข้อมูลใน column ที่เราต้องการจะให้ทานาย ใน
ไฟล์ csv
•แก้ไฟล์จาก csv เป็น arff
•หลังจากนั้นนา attribute ของไฟล์หลัก(.arff)มาใส่ในไฟล์ deploy
(.arff)
20

21. Classify(ต่อ)
หลังจากนั้นโหลดไฟล์หลัก .arff
เข้ามา เพื่อจะทาการ save
model ที่จะใช้ในการ classify
ข้อมูล ไปที่
tab classify >
functions >
MulyilayerPerceptron
21

22. Classify(ต่อ)
กด Start
22

23. Classify(ต่อ)
ทาการ Save model
23

24. Classify(ต่อ)
ทาการ Load model ที่
Save ไว้ก่อนหน้านี้
24

25. Classify(ต่อ)
Load model ชื่อ as1.model
25

26. ใส่แว่นไหม
Test Option
>Supplied test set >กด Set…
26

27. ใส่แว่นไหม(ต่อ)
เลือกไฟล์ที่จะ
deploy โดยคาถาม
คือใส่แว่นไหม >
Close
27

28. ใส่แว่นไหม(ต่อ)
หลังจากนั้น คลิกขวาที่
model และกด
Re-evaluate
model on
current test
set
28

29. ใส่แว่นไหม(ต่อ)
หลังจากนั้น คลิกขวาที่
model อีกที และ
กด Visualize
classifier
errors > save
ข้อมูล
29

30. ใส่แว่นไหม(ต่อ)
เพื่อที่จะ View
ข้อมูลให้ไปที่ Tools
> ArffViewer
> Open > เลือก
ไฟล์ที่save และทา
การเปิด
30

31. ใส่แว่นไหม(ต่อ)
นี้คือข้อมูลที่ทานาย
ออกมา
31

32. เป็ นนักกีฬาไหม
•ขั้นตอนการจักเตรียมข้อมูล และวิธีการดาเนินงานเหมือนกับข้อที่แล้ว ดังนั้นข้อ
นี้จะแสดงแค่ผลลัพธ์ที่ทานายได้
นี้คือข้อมูลที่ทานาย
ออกมา 32

33. เป็ นเพศไหน
•ขั้นตอนการจักเตรียมข้อมูล และวิธีการดาเนินงานเหมือนกับข้อที่แล้ว ดังนั้นข้อ
นี้จะแสดงแค่ผลลัพธ์ที่ทานายได้
นี้คือข้อมูลที่ทานาย
ออกมา 33

34. Associate Apriori
Open file ที่จะใช้ และทา
การเลือก tab ที่เป็น
associate หลังจากนั้นกด
คาว่า Apriori กาหนด
Rule = 100
34

35. Associate Apriori(ต่อ)
• Rule
• 1. egg=N 26 ==> chederword=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 2. chederword=N 26 ==> egg=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 3. lm=N 26 ==> chederword=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 4. chederword=N 26 ==> lm=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 5. lm=N 26 ==> egg=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 6. egg=N 26 ==> lm=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 7. battery=N 26 ==> toothpick=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 8. toothpick=N 26 ==> battery=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 9. soap=N 26 ==> gyoza=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 10. gyoza=N 26 ==> soap=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
35

36. Associate Apriori(ต่อ)
• 11. vanilaroll=N 26 ==> gyoza=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 12. gyoza=N 26 ==> vanilaroll=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 13. vanilaroll=N 26 ==> soap=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 14. soap=N 26 ==> vanilaroll=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 15. cornmilk=N 26 ==> tissue=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 16. tissue=N 26 ==> cornmilk=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 17. pepsi=N 26 ==> garlicbread=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 18. garlicbread=N 26 ==> pepsi=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 19. friedrice=N 26 ==> chickenrice=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 20. chickenrice=N 26 ==> friedrice=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
36

37. Associate Apriori(ต่อ)
• 21. salmon=N 26 ==> chickenrice=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 22. chickenrice=N 26 ==> salmon=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 23. salmon=N 26 ==> friedrice=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 24. friedrice=N 26 ==> salmon=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 25. nori=N 26 ==> lipton=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 26. lipton=N 26 ==> nori=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 27. egg=N lm=N 26 ==> chederword=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0]
conv:(0.96)
• 28. chederword=N lm=N 26 ==> egg=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0]
conv:(0.96)
• 29. chederword=N egg=N 26 ==> lm=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0]
conv:(0.96)
• 30. lm=N 26 ==> chederword=N egg=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0]
37

38. Associate Apriori(ต่อ)
• 31. egg=N 26 ==> chederword=N lm=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 32. chederword=N 26 ==> egg=N lm=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 33. soap=N vanilaroll=N 26 ==> gyoza=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 34. gyoza=N vanilaroll=N 26 ==> soap=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 35. gyoza=N soap=N 26 ==> vanilaroll=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 36. vanilaroll=N 26 ==> gyoza=N soap=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 37. soap=N 26 ==> gyoza=N vanilaroll=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 38. gyoza=N 26 ==> soap=N vanilaroll=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0] conv:(0.96)
• 39. friedrice=N salmon=N 26 ==> chickenrice=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0]
conv:(0.96)
• 40. chickenrice=N salmon=N 26 ==> friedrice=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0]
conv:(0.96)
38

39. Associate Apriori(ต่อ)
• 41. chickenrice=N friedrice=N 26 ==> salmon=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0]
conv:(0.96)
• 42. salmon=N 26 ==> chickenrice=N friedrice=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0]
conv:(0.96)
• 43. friedrice=N 26 ==> chickenrice=N salmon=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0]
conv:(0.96)
• 44. chickenrice=N 26 ==> friedrice=N salmon=N 26 <conf:(1)> lift:(1.04) lev:(0.04) [0]
conv:(0.96)
• 45. coke=N 25 ==> chederword=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 46. coke=N 25 ==> egg=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 47. coke=N 25 ==> lm=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 48. bueger=N 25 ==> chederword=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 49. bueger=N 25 ==> egg=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 50. bueger=N 25 ==> lm=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
39

40. Associate Apriori(ต่อ)
• 51. bueger=N 25 ==> chickenrice=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 52. bueger=N 25 ==> friedrice=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 53. bueger=N 25 ==> salmon=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 54. oishi=N 25 ==> voice=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 55. larbmoorice=N 25 ==> chickenrice=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 56. larbmoorice=N 25 ==> friedrice=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 57. larbmoorice=N 25 ==> salmon=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 58. gulp=N 25 ==> lipton=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 59. gulp=N 25 ==> nori=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 60. ice=N 25 ==> pureriku=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
40

41. Associate Apriori(ต่อ)
• 61. milk=N 25 ==> sandwich=N 25 <conf:(1)> lift:(1.08) lev:(0.07) [1] conv:(1.85)
• 62. sandwich=N 25 ==> milk=N 25 <conf:(1)> lift:(1.08) lev:(0.07) [1] conv:(1.85)
• 63. lay=N 25 ==> lipton=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 64. lay=N 25 ==> nori=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 65. yogurt=N 25 ==> lipton=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 66. yogurt=N 25 ==> nori=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 67. yogurt=N 25 ==> twety=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 68. coke=N egg=N 25 ==> chederword=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 69. coke=N chederword=N 25 ==> egg=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 70. coke=N 25 ==> chederword=N egg=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
41

42. Associate Apriori(ต่อ)
• 71. coke=N lm=N 25 ==> chederword=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 72. coke=N chederword=N 25 ==> lm=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 73. coke=N 25 ==> chederword=N lm=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 74. coke=N lm=N 25 ==> egg=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 75. coke=N egg=N 25 ==> lm=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 76. coke=N 25 ==> egg=N lm=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 77. bueger=N egg=N 25 ==> chederword=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 78. chederword=N bueger=N 25 ==> egg=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 79. bueger=N 25 ==> chederword=N egg=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
42

43. Associate Apriori(ต่อ)
• 81. chederword=N bueger=N 25 ==> lm=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 82. bueger=N 25 ==> chederword=N lm=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 83. bueger=N chickenrice=N 25 ==> chederword=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 84. chederword=N chickenrice=N 25 ==> bueger=N 25 <conf:(1)> lift:(1.08) lev:(0.07) [1] conv:(1.85)
• 85. chederword=N bueger=N 25 ==> chickenrice=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 86. bueger=N 25 ==> chederword=N chickenrice=N 25 <conf:(1)> lift:(1.08) lev:(0.07) [1] conv:(1.85)
• 87. bueger=N friedrice=N 25 ==> chederword=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 88. chederword=N friedrice=N 25 ==> bueger=N 25 <conf:(1)> lift:(1.08) lev:(0.07) [1] conv:(1.85)
• 89. chederword=N bueger=N 25 ==> friedrice=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0] conv:(0.93)
• 90. bueger=N 25 ==> chederword=N friedrice=N 25 <conf:(1)> lift:(1.08) lev:(0.07) [1] conv:(1.85)
43

44. Associate Apriori(ต่อ)
• 91. bueger=N salmon=N 25 ==> chederword=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 92. chederword=N salmon=N 25 ==> bueger=N 25 <conf:(1)> lift:(1.08) lev:(0.07) [1]
conv:(1.85)
• 93. chederword=N bueger=N 25 ==> salmon=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 94. bueger=N 25 ==> chederword=N salmon=N 25 <conf:(1)> lift:(1.08) lev:(0.07) [1]
conv:(1.85)
• 95. egg=N toothpick=N 25 ==> chederword=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 96. chederword=N toothpick=N 25 ==> egg=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 97. egg=N battery=N 25 ==> chederword=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
conv:(0.93)
• 98. chederword=N battery=N 25 ==> egg=N 25 <conf:(1)> lift:(1.04) lev:(0.03) [0]
44