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1 of 40
1. เลือก Explorer
1. เปิดไฟล์.csv
2. Instance = 40
Attributes= 79
4.อัลกอริทึมเลือกเป็นอะไรก็ได้
5.เลือก Use TrainingSet
6. เลือก Start
3. เลือก Classity
7. เลือก Visualize Classifire errors
8. ทำกำร Save จะได้ไฟล์ .arff
1. เปิดไฟล์recript_for_train.arff
2. Instance = 40
Attributes= 79
4.อัลกอริทึมเลือกเป็นอะไรก็ได้
5.เลือก Use TrainingSet
6. เลือก Start
3. เลือก Classity
3. เลือก Classify
4. เลือกเป็น MultilayerPerceptron
5. กำหนด GUI เป็น True
6. กด Start
7. กด Start รอจนรันเสร็จและกด Accept
8. ทำกำร Save Model
9. ทำกำร Load Model ที่ได้Save ไว้
ในข้อก่อนหน้ำ
10. เลือก Re-evaluate model on current test set
11. เลือก Visualize classifier errors
12. Save
13. เลือก ArffViewer
14. เปิดไฟล์ที่เรำทำกำรSaveไว้ในข้อ 13
1. เปิดไฟล์ที่ต้องกำรจะClustering .artt
2. Instance =58
Attributes= 9
3. เลือกแท็บ Cluster
4. เลือกอัลกอริทึมเป็น
SimpleKMean
5. กำหนด Numcluster
6. กด Startและวนทำ ข้อ 5 และ 6 จนได้
Numcluster = 25
7. นำค่ำ Squared error มำ
plot กรำฟ
NumCluster Squared Errors
2 147
3 131
4 126
5 123
6 119
7 107
8 104
9 100
10 98
11 96
12 92
13 89
NumCluster Squared Errors
14 88
15 86
16 84
17 82
18 81
19 78
20 76
21 75
22 74
23 70
24 67
25 64
0
20
40
60
80
100
120
140
160
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Knee Curve = 22
สำมำรถแบ่งกลุ่มได้ทั้งหมด22 กลุ่ม เนื่องจำกค่ำ Squared error ในช่วง
NumCluster 22 มีลักษณะลู่ลงเป็นรูปทรงของKnee Curve
1. เปิดไฟล์ที่ต้องกำรจะ Association
2. Attribute = 26
Instances = 58
3. เลือกแทป Associate
4. กำหนดอัลกอริทึมเป็น Apriori
5. กด Start
ผลลัพธ์
Association Rule Confidence
SaltedPeanutsKohkae-->UHTFreshMilkForemost2000ml 1.00
UHTFreshMilkForemost2000ml-->SaltedPeanutsKohkae 1.00
CheeseCornae-->UHTFreshMilkForemost2000ml 1.00
UHTFreshMilkForemost2000ml-->CheeseCornae 1.00
CheeseCornae-->SaltedPeanutsKohkae 1.00
SaltedPeanutsKohkae-->CheeseCornae 1.00
BigCola1500ml-->ChickenFrankfurtFeetLong 1.00
ChickenFrankfurtFeetLong-->BigCola1500ml 1.00
CroissantSandwichHamAndCheese-->ChickenFrankfurtFeetLong 1.00
ChickenFrankfurtFeetLong-->CroissantSandwichHamAndCheese 1.00

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