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Clustering

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.

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Clustering

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  1. Clustering

  2. 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

  3. Ilustrasi

  4. Classification vs Clustering • Classification

  5. Classification

  6. Classification vs Clustering • Clustering

  7. Clustering

  8. Karakteristik Clustering

  9. Partitioning Clustering • Disebutjuga Exclusive Clustering • Memungkinkansetiap data untukmasukkedalam cluster tertentupadasuatutahapanproses clustering danpadatahapanberikutnyaberpindahke cluster lain. • Contoh : K-Means

  10. Hierarchical Clustering • Suatu data yang masukkedalamsuatu cluster padasuatutahapanprosestidakbisaberpindahke cluster lain padatahapanprosesberikutnya. • Contoh : Agglomerative Clustering.

  11. Overlapping Clustering • Setiap data dapatmenjadianggotadaribeberapa cluster sekaligusberdasarkannilai/derajatkeanggotaannya. • Contoh : Fuzzy C-Means, Gaussian Mixture

  12. Hierarchical Clustering

  13. Hierarchical Clustering • Clustering denganpendekatanhirarkimengelompokkan data yang memilikikemiripankarakteristikdalamhirarki yang samadan yang tidakmiripdalamhirarki yang berbeda.

  14. Metode

  15. Algoritma Agglomerative Clustering • MulaidenganN cluster, setiap cluster mengandungentititunggaldansebuahmatrikssimetrikdarijarak (similarities) D = {dik} dengantipeNxN. • Carimatriksjarakuntukpasangan cluster yang terdekat (paling mirip). Misalkanjarakantara cluster U dan V yang paling miripadalahduv. • Gabungkancluster U dan V. Label cluster yang barudibentukdengan (UV).

  16. Algoritma Agglomerative Clustering (lanjutan) Updateentries pada matrikjarakdengan cara : • Hapus baris dan kolom yang bersesuaian dengan cluster U dan V • Tambahkanbarisdankolom yang memberikanjarak-jarakantaracluster (UV) dan cluster-cluster yang tersisa. • Ulangi langkah 2 dan 3 sebanyak (N-1) kali. (Semua objek akan berada dalam cluster tunggalsetelahalgoritmaberahir). Catatidentitasdari cluster yang digabungkandantingkat-tingkat (jarakatausimilaritas) dimanapenggabunganterjadi.

  17. Proses Clustering

  18. JarakAntar Cluster

  19. Single Linkage

  20. Complete Linkage

  21. Average Linkage

  22. Contoh (Single Linkage)

  23. Jarak Euclidean

  24. MatriksJarak

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

  26. 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

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