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1 Applied Multivariate Analysis Cluster Analysis.

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Presentasi berjudul: "1 Applied Multivariate Analysis Cluster Analysis."— Transcript presentasi:

1 1 Applied Multivariate Analysis Cluster Analysis

2 2 Tujuan Utama Mengambil sejumlah observasi dan membuat pengelompokkan unit-unit, sehingga unit-unit yg berada dlm satu kelompok mempunyai sifat sama dan unit antar kelompok mempunyai sifat berbeda.

3 3 Think About It Homogeneous subgroups are not the same as naturally occurring clusters Homogeneous, but not natural clusters …

4 4 Hal-hal yg diperhatikan ► Beberapa ukuran similaritas (kedekatan) dan dissimilaritas unit-unit.  Euclidian  Mahallanobis ► Penentuan cluster (banyak cluster)  Hierarki  Non Hierarki

5 5 Konsep Hierarki ► Hasil pengelompokkan alammi ► Hasil pengelompokkan merupakan pengabungan: contoh, lima kluster diperoleh dari penggabungan 2 kluster dari enam kluster. ► Metode agglomerasi – setiap observasi adalah cluster dimulai dengan menggabungkan ► Metode divisive (lawan agglomerasi)

6 6 Konsep partisi ► Mempartisi observasi kedalam cluster- cluster sehingga homogen dlm cluster. ► Bukan konsep penggabungan. ► Final cluster masih belum terpisah benar.

7 7 Nearest Neighbors Method Start with N clusters Form a cluster from the two closest points Think of this new cluster as a “point” and define the distance from any point to it as the minimum distance to any point in it. Do until all points are placed in a single cluster Single linkage method

8 8 Nearest Neighbors Example Pairwise distances between six points C 0 ={[1],[2],[3],[4],[5],[6]} C 1 ={[1],[2],[3,5],[4],[6]}

9 9 Nearest Neighbors Example Pairwise distances between five “points” C 0 ={[1],[2],[3],[4],[5],[6]} C 1 ={[1],[2],[3,5],[4],[6]} C 2 ={[1],[2],[3,5,6],[4]} smallest

10 10 Nearest Neighbors Example Pairwise distances between four “points” C 0 ={[1],[2],[3],[4],[5],[6]} C 1 ={[1],[2],[3,5],[4],[6]} C 2 ={[1],[2],[3,5,6],[4]} C 3 ={[1],[2,4],[3,5,6]} smallest

11 11 Nearest Neighbors Example Pairwise distances between three “points” C 0 ={[1],[2],[3],[4],[5],[6]} C 1 ={[1],[2],[3,5],[4],[6]} C 2 ={[1],[2],[3,5,6],[4]} C 3 ={[1],[2,4],[3,5,6]} C 4 ={[2,4],[1,3,5,6]} Single Cluster smallest

12 Prosedur K-Mean Cluster 12

13 Aplikasi (SPSS) ► Two Step cluster  Eksplorasi  Banyak kluster berdasarkan nilai AIC/ BIC ► Hierarki  Single linkage  Complete linkage ► Non Hierarki (K-mean) 13

14 Two step cluster ► Data car_sales.sav ► Catagorical variable : vehicle type ► Continous variable :Price in thousands – feul efficiency  Plot Rank of variable important :by variables and confidence level level  Output AIC / BIC 14

15 Output SPSS Two Step Cluster 15

16 Distribusi kluster 16 Pivot (double klik centroid) : Pivoting trays (buat struktur berikut)

17 Deskripsi Tiap Kluster ► 17

18 Cluster Hierarki ► Data : car_sales ► Select cases : if conditional (type =0) & (sales >100) ► Analyse> classify>hierarchical cluster  Price in thousands through Fuel efficiency as analysis variables.  Select Model as the case labeling variable ► Plot > Dendogram ► Method > Nearest neighbor, Zscore 18

19 Output cluster hierarki ► 19

20 ► Aplikasi Minitab : cereal.mtw  Survey tentang merk dan kandungan gizi cereal  Akan dilakukan pengelompokkan merk dan kandungan gizi ► 20


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