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> library(cluster)
> x=iris[,1:4]
> kc=pam(x,3)
> kc
Medoids:
ID Sepal.Length Sepal.Width Petal.Length Petal.Width
[1,] 8 5.0 3.4 1.5 0.2
[2,] 79 6.0 2.9 4.5 1.5
[3,] 113 6.8 3.0 5.5 2.1
Clustering vector:
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[75] 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 3 3 3 2 3 3 3 3
[112] 3 3 2 2 3 3 3 3 2 3 2 3 2 3 3 2 2 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 2 3
[149] 3 2
Objective function:
build swap
0.6709391 0.6542077
Available components:
[1] "medoids" "id.med" "clustering" "objective" "isolation"
[6] "clusinfo" "silinfo" "diss" "call" "data"
R与数据分析旧笔记(十五) 基于有代表性的点的技术:K中心聚类法
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原文地址:http://www.cnblogs.com/XBlack/p/4886229.html