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Clustering

Clustering

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Clustering. Clustering. Cluster : a collection of objects which are simmilar between them and dissimilar to the objects belonging to other cluster Clustering : the process of organizing objects into groups whose members are similar in some way. Ilustrasi. Classification vs Clustering. - PowerPoint PPT Presentation

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Page 1: Clustering

Clustering

Page 2: Clustering

Clustering

• Cluster : a collection of objects which are simmilar between them and dissimilar to the objects belonging to other cluster

• Clustering : the process of organizing objects into groups whose members are similar in some way

Page 3: Clustering

Ilustrasi

Page 4: Clustering
Page 5: Clustering
Page 6: Clustering
Page 7: Clustering

Classification vs Clustering

• Classification

Page 8: Clustering

Classification

Page 9: Clustering

Classification vs Clustering

• Clustering

Page 10: Clustering

Clustering

Page 11: Clustering

Karakteristik Clustering

Clustering

Partitioning

Hierarchical

Overlapping

Page 12: Clustering

Partitioning Clustering

• Disebut juga Exclusive Clustering

• Memungkinkan setiap data untuk masuk ke dalam cluster tertentu pada suatu tahapan proses clustering dan pada tahapan berikutnya berpindah ke cluster lain.

• Contoh : K-Means

Page 13: Clustering

Hierarchical Clustering

• Suatu data yang masuk ke dalam suatu cluster pada suatu tahapan proses tidak bisa berpindah ke cluster lain pada tahapan proses berikutnya.

• Contoh : Agglomerative Clustering.

Page 14: Clustering

Overlapping Clustering

• Setiap data dapat menjadi anggota dari beberapa cluster sekaligus berdasarkan nilai/derajat keanggotaannya.

• Contoh : Fuzzy C-Means, Gaussian Mixture

Page 15: Clustering

Hierarchical Clustering

Page 16: Clustering

Hierarchical Clustering

• Clustering dengan pendekatan hirarki mengelompokkan data yang memiliki kemiripan karakteristik dalam hirarki yang sama dan yang tidak mirip dalam hirarki yang berbeda.

Page 17: Clustering

Metode

Hierarchical Clustering

Divisive

Top to down division (1 to N cluster)

Agglomerative

Down to top merge (N to k cluster)

Page 18: Clustering

Algoritma Agglomerative

Clustering

1. Mulai dengan N cluster, setiap cluster mengandung entiti tunggal dan sebuah matriks simetrik dari jarak (similarities) D = {dik} dengan tipe NxN.

2. Cari matriks jarak untuk pasangan cluster yang terdekat (paling mirip). Misalkan jarak antara cluster U dan V yang paling mirip adalah duv.

3. Gabungkan cluster U dan V. Label cluster yang baru dibentuk dengan (UV).

Page 19: Clustering

Algoritma Agglomerative

Clustering (lanjutan)

Update entries pada matrik jarak dengan cara :• Hapus baris dan kolom yang bersesuaian dengan

cluster U dan V• Tambahkan baris dan kolom yang memberikan

jarak-jarak antara cluster (UV) dan cluster-cluster yang tersisa.

4. Ulangi langkah 2 dan 3 sebanyak (N-1) kali. (Semua objek akan berada dalam cluster tunggal setelah algoritma berahir). Catat identitas dari cluster yang digabungkan dan tingkat-tingkat (jarak atau similaritas) di mana penggabungan terjadi.

Page 20: Clustering

Proses Clustering

Page 21: Clustering

Jarak Antar Cluster

Page 22: Clustering

Single Linkage

Page 23: Clustering

Complete Linkage

Page 24: Clustering

Average Linkage

Page 25: Clustering

Contoh (Single Linkage)

x yp1 0.40 0.53p2 0.22 0.38p3 0.35 0.32p4 0.26 0.19p5 0.08 0.41p6 0.45 0.30

Page 26: Clustering

Jarak Euclidean

Page 27: Clustering

Matriks Jarak

p1 0p2 0.23 0p3 0.22 0.15 0p4 0.37 0.20 0.15 0p5 0.34 0.14 0.28 0.29 0p6 0.23 0.25 0.11 0.22 0.39 0

p1 p2 p3 p4 p5 p6

Page 28: Clustering
Page 29: Clustering

p1 0p2 0.24 0(p3, p6) 0.22 0.15 0p4 0.37 0.20 0.15 0p5 0.34 0.14 0.28 0.29 0

p1 p2 (p3, p6) p4 p5

dist( (p3, p6), p1 ) = MIN ( dist(p3, p1) , dist(p6, p1) ) = MIN ( 0.22 , 0.23 )

= 0.22

Page 30: Clustering
Page 31: Clustering

p1 0(p2, p5) 0.24 0(p3, p6) 0.22 0.15 0p4 0.37 0.20 0.15 0

p1 (p2, p5) (p3, p6) p4

dist( (p3, p6), (p2, p5) ) = MIN ( dist(p3, p2) , dist(p6, p2), dist(p3, p5), dist(p6, p5) ) = MIN ( 0.15 , 0.25, 0.28, 0.39 )

= 0.15 

Page 32: Clustering
Page 33: Clustering

p1 0(p2, p5, p3, p6) 0.22 0

p4 0.37 0.15 0p1 (p2, p5, p3, p6) p4

Page 34: Clustering

p1 0(p2, p5, p3, p6, p4) 0.22 0

p1 (p2, p5, p3, p6, p4)

Page 35: Clustering

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