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R Programming - Need to use HARTIGAN dataset directory that contains test data for clustering algorithms

R Programming - Need to use HARTIGAN dataset directory that contains test data for clustering algorithms

  • 26th Aug, 2021
  • 18:25 PM

#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)


 

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