拜金主义—你一定要买最贵的车吗？

1、拜金主义你一定要买最贵的车吗？1.数据准备使用MASS包里的Cars93数据集，该数据集由30个汽车品牌下的93种汽车构成93条观测。2.数据处理将数据集导出成excel格式，运用SPSS软件，将除了价格之外的变量标准化处理，并取出方差贡献率的前四个变量，计算出标准得分后，随后运用价格和标准得分进行聚类分析。3.软件实现对处理后的数值进行聚类分析read.csv(C:/Users/admin/Desktop/cars.csv)Cars93=read.csv(C:/Users/admin/Desktop/cars.csv)View(Cars93)var=Cars93$Namevar=as.cha

2、racter(var)head(var)for(i in 1:93) s(Cars93)i=varihead(Cars93)plot(Cars93$Price,Cars93$Function)C=which(Cars93$Name=奥迪 90)T=which(Cars93$Name=宝马 535i)H=which(Cars93$Name=雪佛兰 Astro)I=which(Cars93$Name=道奇 Spirit)U=which(Cars93$Name=林肯 Continental)M=which.max(Cars93$Price)points(Cars93c(C,T,H,I

3、,U,M),-1,pch=16)legend(Cars93$PriceC-1.2,Cars93$FunctionC,奥迪 90,bty=n,xjust=0,yjust=0.5,cex=0.8)legend(Cars93$PriceT,Cars93$FunctionT,宝马 535i,bty=n,xjust=0.5,cex=0.8)legend(Cars93$PriceH,Cars93$FunctionH,雪佛兰 Astro,bty=n,xjust=0.5,cex=0.8)legend(Cars93$PriceI-1.2,Cars93$FunctionI,道奇 Spirit,bty=n,xjus

4、t=0,yjust=0.5,cex=0.8)legend(Cars93$PriceU,Cars93$FunctionU,林肯 Continental,bty=n,xjust=0.5,yjust=0,cex=0.8)legend(Cars93$PriceM,Cars93$FunctionM,Cars93$FunctionM,bty=n,xjust=1,cex=0.8)图一图二# k-means #fit_km1=kmeans(Cars93,-1,center=3)print(fit_km1)fit_km1$centersfit_km1$totss;fit_km1$tot.withinss;fit

5、_km1$betweenssfit_km1$betweenss+fit_km1$tot.withinssplot(Cars93,-1,pch=(fit_km1$cluster-1)points(fit_km1$centers,pch=8)legend(fit_km1$centers1,1,fit_km1$centers1,2,Center_1,bty=n,xjust=1,yjust=0,cex=0.8)legend(fit_km1$centers2,1-2,fit_km1$centers2,2,Center_2,bty=n,xjust=0,yjust=0,cex=0.8)legend(fit_

6、km1$centers3,1,fit_km1$centers3,2,Center_3,bty=n,xjust=0.5,cex=0.8)for(i in 1:7) var=c(C,T,H,I,U,M)points(Cars93vari,-1,pch=fit_km1$clustervari+14) legend(Cars93$PriceC-1.2,Cars93$FunctionC,奥迪 90,bty=n,xjust=0,yjust=0.5,cex=0.8)legend(Cars93$PriceT,Cars93$FunctionT,宝马 535i,bty=n,xjust=0.5,cex=0.8)le

7、gend(Cars93$PriceH,Cars93$FunctionH,雪佛兰 Astro,bty=n,xjust=0.5,cex=0.8)legend(Cars93$PriceI-1.1,Cars93$FunctionI,道奇 Spirit,bty=n,xjust=0,yjust=0.5,cex=0.8)legend(Cars93$PriceU,Cars93$FunctionU,林肯 Continental,bty=n,xjust=0.5,yjust=0,cex=0.8)legend(Cars93$PriceM,Cars93$FunctionM,Cars93$NameM,bty=n,xjus

8、t=1,cex=0.8)图三图四#聚类优度#result=rep(0,92)for(k in 1:92) fit_km=kmeans(Cars93,-1,center=k) resultk=fit_km$betweenss/fit_km$totssplot(1:92,result,type=b,main=Choosing the Optimal Number of Cluster, xlab=number of cluster: 1 to 67,ylab=betweenss/totss)points(10,result10,pch=16)legend(10,result10,paste(10,

9、sprintf(%.1f%,result10*100),),sep=),bty=n,xjust=0.3,cex=0.8)fit_km2=kmeans(Cars93,-1,center=10)cluster_90=fit_km2$clusterwhich(Cars93$Name=奥迪 90)which(fit_km2$cluster=cluster_90)图五图六# k-Medoids #library(cluster)fit_pam=pam(Cars93,-1,3)print(fit_pam)head(fit_pam$data)fit_pam$callfit_pam1=pam(Cars93,-

10、1,3,keep.data=FALSE)fit_pam1$datafit_pam2=pam(Cars93,-1,3,cluster.only=TRUE)print(fit_pam2)which(fit_km$cluster!=fit_pam$cluster)plot(Cars93,-1,pch=(fit_pam$cluster-1)c1=which(rownames(Cars93)=rownames(fit_pam$medoids)1)c2=which(rownames(Cars93)=rownames(fit_pam$medoids)2)c3=which(rownames(Cars93)=r

11、ownames(fit_pam$medoids)3)for(i in 1:3) var=c(c1,c2,c3)points(Cars93vari,-1,pch=fit_pam$clustervari+14) legend(fit_pam$medoids1,1,fit_pam$medoids1,2,paste(Center_1:,rownames(fit_pam$medoids)1),bty=n,xjust=0.5,yjust=0,cex=0.8)legend(fit_pam$medoids2,1,fit_pam$medoids2,2,paste(Center_2:,rownames(fit_p

12、am$medoids)2),bty=n,xjust=0,yjust=0.5,cex=0.8)legend(fit_pam$medoids3,1,fit_pam$medoids3,2,paste(Center_3:,rownames(fit_pam$medoids)3),bty=n,xjust=0.5,yjust=0,cex=0.8)points(Cars93c(21,23,33),-1,pch=12)legend(Cars93$Price21,Cars93$Function21,克莱斯勒 Concorde,bty=n,xjust=0.5,yjust=0,cex=0.8)legend(Cars9

13、3$Price23-1.2,Cars93$Function23,克莱斯勒 Imperial,bty=n,xjust=0,yjust=0.5,cex=0.8)legend(Cars93$Price33-1.2,Cars93$Function33,福特 Escort,bty=n,xjust=0,yjust=0.5,cex=0.8)图七图八# HC #fit_hc=hclust(dist(Cars93,-1)print(fit_hc)plot(fit_hc)group_k3=cutree(fit_hc,k=3)group_k3table(group_k3)group_h18=cutree(fit_h

14、c,h=18)group_h18table(group_h18)sapply(unique(group_k3),function(g)Cars93$Namegroup_k3=g)plot(fit_hc)rect.hclust(fit_hc,k=4,border=red)rect.hclust(fit_hc,k=3,border=blue)rect.hclust(fit_hc,k=7,which=c(2,6),border=green)图九图十总结：据以上分析可知，聚类结果可分为三类，第二类为价格高，性能高的汽车；第一类为价格中等，性能高的汽车；第三类为价格低性能低的汽车。以中心聚类结果为代表来看，三个中心点分别为第2个样本 讴歌传奇，价格为33.9万，性能优度为0.576；第56个样本马自达 MPV ，价格19.1万，性能优度为0.499；第79个样本 土星SL，价格11.1万，性能优度为负0.581；由图八也可以看出以马自达MPV为中心点的第一类汽车大多数性能与第二类相差不大，甚至有一些的性能超过了第二类的汽车，但是价格却便宜了8万到15万不等，而且第二类汽车分布较散，说明此类轿车针对的目标客户不是普通大众，而是收入较高的群体，且此类轿车的品牌大多为人们所知晓的高端轿车品