# Applied Multivariate Analysis

## Presentasi berjudul: "Applied Multivariate Analysis"— Transcript presentasi:

Applied Multivariate Analysis
Cluster Analysis

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.

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

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

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)

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

Nearest Neighbors Method
Single linkage method Do until all points are placed in a single cluster 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.

Nearest Neighbors Example
Pairwise distances between six points C0={[1],[2],[3],[4],[5],[6]} C1={[1],[2],[3,5],[4],[6]}

Nearest Neighbors Example
Pairwise distances between five “points” C0={[1],[2],[3],[4],[5],[6]} C1={[1],[2],[3,5],[4],[6]} C2={[1],[2],[3,5,6],[4]} smallest

Nearest Neighbors Example
Pairwise distances between four “points” C0={[1],[2],[3],[4],[5],[6]} C1={[1],[2],[3,5],[4],[6]} C2={[1],[2],[3,5,6],[4]} C3={[1],[2,4],[3,5,6]} smallest

Nearest Neighbors Example
Pairwise distances between three “points” C0={[1],[2],[3],[4],[5],[6]} C1={[1],[2],[3,5],[4],[6]} C2={[1],[2],[3,5,6],[4]} C3={[1],[2,4],[3,5,6]} C4={[2,4],[1,3,5,6]} smallest Single Cluster

Prosedur K-Mean Cluster

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

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 Output AIC / BIC

Output SPSS Two Step Cluster

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

Deskripsi Tiap Kluster

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

Output cluster hierarki

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

Presentasi serupa