- 26th Aug 2021
- 06:03 am
#Q2. Perform K-means clustering on file19.txt on the above web page.
library(factoextra) library(fpc) library(dplyr)
#Q2.2. K-means clustering (2.5 points divided evenly among the components)
clust<-read.csv("Cluster.csv")
str(clust)
cluste<-scale(clust[-1])
cluster<-data.frame(cluste)
rownames(cluster)<-clust$Name
res.dst<-get_dist(cluster, method="pearson")
fviz_dist(res.dst,lab_size=8)
res.km<-eclust(cluster, "kmeans",nstart=20)
fviz_gap_stat(res.km$gap_stat)
fviz_silhouette(res.km)
res.km$nbclust
fviz_nbclust(res.km)
fviz_cluster(res.km)
clusters<-res.km$cluster
Clust1<-cbind(cluster, clusters)
table(Clust1$clusters)
aggregate(clust[-1], by=list(cluster=res.km$cluster), mean)
#Q2.3. Hierarchical clustering (3 points divided evenly among the components)
set.seed(1122)
clust_1<-sample(cluster,size=35, replace=TRUE)
rownames(clust_1)<-clust$Name
res.hclust<-eclust(clust_1, "hclust", hc_method="single")
fviz_dend(res.hclust, rect = TRUE)
fviz_silhouette(res.hclust)
fviz_cluster(res.hclust)
fviz_dend(res.hclust)
res.hclust_2<-eclust(clust_1[,2:9], "hclust", hc_method="complete")
fviz_dend(res.hclust_2, rect = TRUE)
fviz_silhouette(res.hclust_2)
fviz_cluster(res.hclust_2)
fviz_dend(res.hclust_2)
res.hclust_3<-eclust(clust_1[,2:9], "hclust", hc_method="average")
fviz_dend(res.hclust_3, rect = TRUE)
fviz_silhouette(res.hclust_3)
fviz_cluster(res.hclust_3)
fviz_dend(res.hclust_3)
#Complete produces least singleton sets
res.hclust_4<-eclust(clust_1[,2:9], "hclust",k=3, hc_method="complete")
fviz_dend(res.hclust_4, rect = TRUE)
fviz_silhouette(res.hclust_4)
fviz_cluster(res.hclust_4)
fviz_dend(res.hclust_4)
