LEARNING. Program Studi Ilmu Komputer FPMIPA UPI RNI IK460(Kecerdasan Buatan)

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1 LEARNING Jiawei Han and Micheline Kamber Data Mining Concepts and Techniques. San Fransisco : Elsevier M.Tim Jones. Artificial Intelligence A System Approach. Slide Kuliah Data Mining - Klasifikasi, Yudi Wibisono Program Studi Ilmu Komputer FPMIPA UPI RNI IK460(Kecerdasan Buatan

2 Pendekatan & Algoritma Supervised learning Perceptrons, backpropagation, decision tree, naive bayes Unsupervised learning Hebbian learning, vector quantization, adaptive resonance theory (ART, nearest neighbor 2

3 DECISION TREE

4 Pemodelan Pohon 4

5 Proses Pemodelan & Klasifikasi A 1 A 2... A n Class Data Training Algoritma Klasifikasi A 1 A 2... A n Class ? ? Data Test Model Klasifikasi prediksi A 1 A 2... A n Class

6 DATA TRAINING age income student credit_rating buys_computer youth high no fair no youth high no excellent no middle_aged high no fair yes senior medium no fair yes senior low yes fair yes senior low yes excellent no middle_aged low yes excellent yes youth medium no fair no youth low yes fair yes senior medium yes fair yes youth medium yes excellent yes middle_aged medium no excellent yes middle_aged high yes fair yes senior medium no excellent no 6

7 Entrophy dan Info Gain Entrophy: kemurnian sembarang atribut dalam sebuah set atau himpunan record data. Data yang homogen memiliki nilai entrophy yang rendah, ditunjukkan dengan selisih jumlah kelas yang signifikan. Entrophy( S I( S p j log p m j1 j Information Gain: pengurangan entropy oleh suatu atribut. Nilai info gain yang tinggi membuat data semakin homogen. 7

8 INFO KELAS Komposisi kelas: Yes : 9 No : 5 Info( D I(9, log log ( ( ( (

9 INFO ATRIBUT AGE Atribut Age Nilai Yes No Info youth 2 3 I (2,3 middle_aged 4 0 I (4,0 senior 3 2 I (3,2 I(4,0 4 4 log ( (0 ( (0 log 2 0 ( 4 I(2, log log ( ( ( ( I(3, log log ( ( ( (

10 INFO ATRIBUT AGE Atribut Age nilai Yes No Jumlah Atribut Info youth I (2, middle_aged I (4,0 0 senior I (3, Jumlah Record 14 Info age ( D I(2,3 I(4,0 I(3, (0.357(0.971 (0.286(0 (0.357( Gain( Age Info( D Info age ( D 10

11 INFO ATRIBUT INCOME Atribut Income Nilai Yes No Info high 2 2 I (2,2 medium 4 2 I (4,2 low 3 1 I (3,1 I(2, log log ( ( 1 ( ( ( 4 I(4, log log ( ( ( ( I(3, log log ( ( ( (

12 INFO ATRIBUT INCOME Atribut Income nilai Yes No Jumlah Atribut Info high I (2,2 1 medium I (4, low I (3, Jumlah Record 14 Info income I(2,2 I(4,2 I(3, (0.286(1 (0.429(0.918 (0.286( ( D Gain( Income Info( D Info income ( D 12

13 INFO ATRIBUT STUDENT Atribut Student Nilai Yes No Info yes 6 1 I (6,1 no 3 4 I (3,4 I(6, log log ( ( ( ( I(3, log log ( ( ( (

14 INFO ATRIBUT STUDENT Atribut Student nilai Yes No Jumlah Atribut Info Yes I (6, No I (3, Jumlah Record 14 Info student 7 7 I(6,1 I(3, (0.5(0.592 (0.5( ( D Gain( Student Info( D Info student ( D 14

INFO ATRIBUT CREDIT-RATING Atribut Credit-Rating Nilai Yes No Info Fair 6 2 I (6,2 Excellent 3 3 I (3,3 I(6,2 6 6 2 2

15 INFO ATRIBUT CREDIT-RATING Atribut Credit-Rating Nilai Yes No Info Fair 6 2 I (6,2 Excellent 3 3 I (3,3 I(6, log log ( ( ( ( I(3, log log ( ( 1 ( ( ( 6 15

16 INFO ATRIBUT CREDIT-RATING Atribut Credit-Rating nilai Yes No Jumlah Atribut Info Fair I (6, Excellent I (3, Jumlah Record 14 Info student 8 6 I(6,2 I(3, (0.571(0.592 (0.429( ( D Gain( Credit Info( D Info Rating creditrating ( D 16

17 Model Klasifikasi 17

18 TEOREMA BAYES

19 Peluang Bersyarat # Kuliah (K Tidak Kuliah ( K Perempuan (P Laki-laki (L Semesta ( = = 1000 Kuliah (K = = 600 Perempuan (P = = 600 P (PK = PK = 300 Dengan peluang bersyarat P (PK 1000 = P (P K. P (K =

20 Teorema Bayes P (P K P (K : posterior : a priori P K PK P (P K = P (PK P (K Karena PK = KP P (P K = P (P K = P (K P. P (P P (K P (K P. P (P P (K P. P (P+P (K L. P (L 20

21 Naive Bayes ( ( ( ( A P i P C i A C P A i C P Terdapat sejumlah kelas (C i -> kelas ke i Karena P(A konstan untuk setiap Ci maka bisa ditulis, pencarian max untuk: ( ( ( i P C i A C P A i C P (... ( ( 1 ( ( 2 1 Ci a P Ci a P Ci a P n k Ci a P Ci P n k X

22 DATA TRAINING age income student credit_rating buys_computer youth high no fair no youth high no excellent no middle_aged high no fair yes senior medium no fair yes senior low yes fair yes senior low yes excellent no middle_aged low yes excellent yes youth medium no fair no youth low yes fair yes senior medium yes fair yes youth medium yes excellent yes middle_aged medium no excellent yes middle_aged high yes fair yes senior medium no excellent no 22

23 Latihan Klasifikasi menggunakan algoritma naive bayes, Kelas: C 1 = yes C 2 = no Atribut: A 1 (age = youth A 2 (income = medium A 3 (student = yes A 4 (credit_rating = fair 23

24 Tugas Kelompok Reinforcement Learning ANN: Hopfield Auto-Associative Model ANN: Hebbialn Particle Swarm Optimization Artificial Immune System Fuzzy Quantification 24

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