CLASSIFICATION & PREDICTION K-NEAREST...
Transcript of CLASSIFICATION & PREDICTION K-NEAREST...
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CLASSIFICATION & PREDICTIONK-NEAREST NEIGHBORS
Ericks
Universitas Gunadarma – Konsep Data Mining
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ALGORITMA KNN CLASSIFICATION
Tentukan K (Jumlah tetangga terdekat).
Hitung Jarak antara data yang diuji dengan data training.
Ranking berdasarkan jarak terdekat dan tentukan apakah termasuk dalam K (jumlah tetangga terdekat).
Ambil Class dalam K, dan pilih Class dengan data terbanyak.
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DATA
Mahasiswa EIPK = 3TEMPAT TINGGAL = 2 Grup ?
K = 3
Object Attribute 1 (X)
IPKAttribute 2 (Y)
TEMPAT TINGGAL Group
Mahasiswa A 1 1 1Mahasiswa B 2 1 1Mahasiswa C 4 3 2Mahasiswa D 5 4 2
3 2 ?
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HITUNG JARAK
Object (X) (Y) Group Jarak ke Data (3,2)
Mahasiswa A 1 1 1 (1-3)2 + (1-2)2 = 5
Mahasiswa B 2 1 1 (2-3)2 + (1-2)2 = 2
Mahasiswa C 4 3 2 (4-3)2 + (3-2)2 = 2
Mahasiswa D 5 4 2 (5-3)2 + (4-2)2 = 8
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RANKING
Object (X) (Y) Group Jarak ke Data (3,2) Rank
Mahasiswa A 1 1 1 (1-3)2 + (1-2)2 = 5 2
Mahasiswa B 2 1 1 (2-3)2 + (1-2)2 = 2 1
Mahasiswa C 4 3 2 (4-3)2 + (3-2)2 = 2 1
Mahasiswa D 5 4 2 (5-3)2 + (4-2)2 = 8 3
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DATA YANG TERMASUK DALAM K
Object (X) (Y) GroupJarak ke Data
(3,2) Rank Termasuk dalam K
Mahasiswa A 1 1 1 (1-3)2 + (1-2)2 = 5 2 Y
Mahasiswa B 2 1 1 (2-3)2 + (1-2)2 = 2 1 Y
Mahasiswa C 4 3 2 (4-3)2 + (3-2)2 = 2 1 Y
Mahasiswa D 5 4 2 (5-3)2 + (4-2)2 = 8 3 T
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HASIL
Termasuk dalam K, A Grup 1, B Grup 1, C Grup 2
Gunakan data terbanyak untuk memilih grupKarena jumlah grup 1 (A dan B) lebih banyak dari grup 2 (C), maka data Mahasiswa E (3,2) masuk ke grup 1.
Object (X) (Y) GroupJarak ke Data
(3,2) Rank Termasuk dalam K ?
Mahasiswa A 1 1 1 (1-3)2 + (1-2)2 = 5 2 Y
Mahasiswa B 2 1 1 (2-3)2 + (1-2)2 = 2 1 Y
Mahasiswa C 4 3 2 (4-3)2 + (3-2)2 = 2 1 YMahasiswa D 5 4 2 (5-3)2 + (4-2)2 = 8 3 T
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ALGORITMA KNN PREDICTION
Tentukan K (Jumlah tetangga terdekat).
Hitung Jarak antara data yang diuji dengan data training.
Urutkan berdasarkan jarak terdekat dan tentukan apakah termasuk dalam K (jumlah tetangga terdekat).
Ambil Class dalam K, dan pilih Class dengan data terbanyak.
Hitung Rata-rata dari data terbanyak.
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DATA
Data X (5.0) berapa Y ?
K = 3
(X) (Y)
1.0 18
4.0 12
2.8 17
2.5 27
3.5 8
1.1 22
4.7 24
2.3 15
5.0 ?
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HITUNG JARAK
(X) (Y) Jarak
1.0 18 (5.0 – 1.0)2 = 16
4.0 12 (5.0 – 4.0)2 = 1
2.8 17 (5.0 – 2.8)2 = 4.84
2.5 27 (5.0 – 2.5)2 = 6.25
3.5 8 (5.0 – 3.5)2 = 2.25
1.1 22 (5.0 – 1.1)2 = 15.21
4.7 24 (5.0 – 4.7)2 = 0.09
2.3 15 (5.0 – 2.3)2 = 7.29
5.0 ?
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URUTKAN JARAK
(X) (Y) Jarak #
1.0 18 (5.0 – 1.0)2 = 16 7
4.0 12 (5.0 – 4.0)2 = 1 2
2.8 17 (5.0 – 2.8)2 = 4.84 4
2.5 27 (5.0 – 2.5)2 = 6.25 5
3.5 8 (5.0 – 3.5)2 = 2.25 3
1.1 22 (5.0 – 1.1)2 = 15.21 6
4.7 24 (5.0 – 4.7)2 = 0.09 1
2.3 15 (5.0 – 2.3)2 = 7.29 8
5.0 ?
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DATA YANG TERMASUK DALAM K
(X) (Y) Jarak # Termasuk dalam K
1.0 18 (5.0 – 1.0)2 = 16 7 T
4.0 12 (5.0 – 4.0)2 = 1 2 Y
2.8 17 (5.0 – 2.8)2 = 4.84 4 T
2.5 27 (5.0 – 2.5)2 = 6.25 5 T
3.5 8 (5.0 – 3.5)2 = 2.25 3 Y
1.1 22 (5.0 – 1.1)2 = 15.21 6 T
4.7 24 (5.0 – 4.7)2 = 0.09 1 Y
2.3 15 (5.0 – 2.3)2 = 7.29 8 T
5.0 ?
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1. Metode Simple UnweightedVoting (Menghitung Rata-Rata)Data X (5.0)
2. Metode Weighted Voting (Menghitung Bobot)
Data X (5.0)
HASIL(X) (Y) Jarak # Termasuk dalam K4.0 12 (5.0 – 4.0)2 = 1 2 Y3.5 8 (5.0 – 3.5)2 = 2.25 3 Y4.7 24 (5.0 – 4.7)2 = 0.09 1 Y
5.0 ?
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FUZZY KNN
(X) (Y) Kelas Jarak
1.0 18 1 (5.0 – 1.0)2 = 16
4.0 12 1 (5.0 – 4.0)2 = 1
2.8 17 2 (5.0 – 2.8)2 = 4.84
2.5 27 2 (5.0 – 2.5)2 = 6.25
3.5 8 1 (5.0 – 3.5)2 = 2.25
1.1 22 2 (5.0 – 1.1)2 = 15.21
4.7 24 2 (5.0 – 4.7)2 = 0.09
2.3 15 1 (5.0 – 2.3)2 = 7.29
5.0 ? 1
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FUZZY KNN
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HASIL(X) (Y) Kelas Jarak # Termasuk dalam K4.0 12 1 (5.0 – 4.0)2 = 1 2 Y3.5 8 1 (5.0 – 3.5)2 = 2.25 3 Y4.7 24 2 (5.0 – 4.7)2 = 0.09 1 Y
5.0 ?
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Discovering Knowledge in Data (Introduction to Data Mining), Chapter 5, Daniel T. Larose, Wiley, 2004
REFERENCES