实验报告聚类分析

实验报告聚类分析

实验原理：K均值聚类、中心点聚类、系统聚类和EM算法聚类分析技术。实验题目：用鸢尾花的数据集，进行聚类挖掘分析。

实验要求：探索鸢尾花数据的基本特征，利用不同的聚类挖掘方法，获得基本结论并简明解释。

实验题目--分析报告：data(iris)

> rm(list=ls())

> gc()

used (Mb) gc trigger (Mb) max used (Mb)

Ncells 431730 23.1 929718 49.7 607591 32.5

Vcells 787605 6.1 8388608 64.0 1592403 12.2

> data(iris)

> datav-iris

> head(data)

Sepal.Length Sepal.Width Petal.Length Petal.Width Specie

s

1 5.1 3.5 1.4 0.

2 setosa

2 4.9 3.0 1.4 0.2 setosa

3 4.7 3.2 1.3 0.2 setosa

4 4.6 3.1 1.

5 0.2 setosa

5 5.0 3.

6 1.4 0.2 setosa

6 5.4 3.9 1.

7 0.4 setosa

#Kmean聚类分析

> n ewiris <- iris

> n ewiris$Species <- NULL

> (kc <- kmea ns(n ewiris, 3))

K-mea ns clusteri ng with 3 clusters of sizes 62, 50, 38

Cluster mea ns:

Sepal.Length Sepal.Width Petal.Length Petal.Width

5.901613 2.748387 4.393548 1.433871

5.006000 3.428000 1.462000 0.246000

6.850000 3.073684 5.742105 2.071053

Clusteri ng vector:

[1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

[41] 2 2 2 2 2 2 2 2 2 2 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 3 1 1

[81] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 3 3 3 3 1 3 3 3 3 3 3

1 1 3 3 3 3 1

[121] 3 1 3 1 3 3 1 1 3 3 3 3 3 1 3 3 3 3 1 3 3 3 1 3 3 3 1 3 3 1

With in cluster sum of squares by cluster:

[1] 39.82097 15.15100 23.87947

(between_SS / total_SS = 88.4 %)

Available comp onen ts:

[1] "cluster" "centers" "totss"

"withinss" "tot.withinss" ⑹"betweenss" "size"

"iter" "ifault" > table(iris$Species, kc$cluster)

1 2 3

setosa 0 50 0

versicolor 48 0 2

virgi nica 14 0 36

> plot( newiris[c("Sepal.Le ngth", "Sepal.Width")], col = kc $cluster)

> poi nts(kc$ce nters[,c("Sepal.Le ngth", "Sepal.Width")], c ol = 1:3, pch = 8, cex=2)

#K-Mediods 进行聚类分析

QT 9初 Ort GE

s

> in stall.packages("cluster")

> library(cluster)

> iris.pam<-pam(iris,3)

> table(iris$Species,iris.pam$clusteri ng)

1 2 3

setosa 50 0 0

versicolor 0 3 47

virgi nica 0 49 1

> layout(matrix(c(1,2),1,2))

> plot(iris.pam)

Silhouette plot of pam(x = iris, k = 3) n-150 3 Astels Cj J - s ； 2 52 0.41 0.0 0.2 0.4 D.S 0.6 1.0 Sillwuele width 耳 SiHowHie widWi - 0.57

clusplot(pam(x = iris k = 3^)

Coinpcx 出 nl 1

Triybt? hvo conbfXjrKJi 吒 Cspldin 95 02 %

po ri[

> layout(matrix(1)) -3 -2 -1 0 1 2 3

rl

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