การวิเคราะห์ข้อมูลด้วย Weka

1. SUPUNSA POJANASIN
55102011026 SC32B
การวิเคราะห์ข้อมูลด้วยWEKA

2. Clustering
เตรียมข้อมูลก่อนที่จะนำไปทำกำร clustering
Cluster เลือก SimpleKMeans

3. Clustering
กำหนด numClusters = 2

4. Clustering
กำหนด numClusters = 3

5. Clustering
กำหนด numClusters = 4

6. Clustering
กำหนด numClusters = 5

7. Clustering
กำหนด numClusters = 6

8. Clustering
กำหนด numClusters = 7

9. Clustering
กำหนด numClusters = 8

10. Clustering
กำหนด numClusters = 9

11. Clustering
กำหนด numClusters = 10

12. Clustering
Knee Curve ที่จุดที่ 5
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Chart Title

13. Association Rule โดยใช้ Apriory

14. Association Rule

15. Association Rule
Best rules found:
1. Ice-Cream=t 42 ==> Water=t 42 <conf:(1)>lift:(2.56) lev:(0.2) [25] conv:(25.59)
2. Ice-Cream=t Sandwich=t 36 ==> Water=t 36 <conf:(1)> lift:(2.56) lev:(0.17) [21] conv:(21.94)
3. Milk=t Ice-Cream=t 26 ==> Water=t 26 <conf:(1)> lift:(2.56) lev:(0.12) [15] conv:(15.84)
4. Milk=t Ice-Cream=t Sandwich=t 23 ==> Water=t 23 <conf:(1)> lift:(2.56) lev:(0.11) [14] conv:(14.02)
5. TissuePaper=t Sandwich=t 14 ==> Water=t 14 <conf:(1)> lift:(2.56) lev:(0.07) [8] conv:(8.53)
6. Milk=t Sandwich=t 27 ==> Water=t 26 <conf:(0.96)> lift:(2.47) lev:(0.12) [15] conv:(8.23)
7. MoneyCard=t 25 ==> Bread=t 23 <conf:(0.92)> lift:(3.27) lev:(0.12) [15] conv:(5.99)
8. Sandwich=t 45 ==> Water=t 41 <conf:(0.91)> lift:(2.33) lev:(0.18) [23] conv:(5.48)

16. Classification : Income (Daily,Month)
tranningTime= 500

17. Classification : Income (Daily,Month)
tranningTime= 500 โดยใช้วิธี MultiLayererceptron

18. Classification : Income (Daily,Month)
tranningTime= 500

19. Classification : Income (Daily,Month)
tranningTime= 200

20. Classification : Income (Daily,Month)
tranningTime= 200

21. Classification : Income (Daily,Month)
tranningTime= 200