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R in Action (2nd ed): Chapter 16 #

Cluster analysis #

requires packaged NbClust, flexclust, rattle #

install.packages(c("NbClust", "flexclust", "rattle")) #

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par(ask=TRUE)
opar <- par(no.readonly=FALSE)

Calculating Distances

data(nutrient, package="flexclust")
head(nutrient, 2)
d <- dist(nutrient)
as.matrix(d)[1:4,1:4]

Listing 16.1 - Average linkage clustering of nutrient data

data(nutrient, package="flexclust")
row.names(nutrient) <- tolower(row.names(nutrient))
nutrient.scaled <- scale(nutrient)
d <- dist(nutrient.scaled)
fit.average <- hclust(d, method="average")
plot(fit.average, hang=-1, cex=.8, main="Average Linkage Clustering")

Listing 16.2 - Selecting the number of clusters

library(NbClust)
nc <- NbClust(nutrient.scaled, distance="euclidean",
min.nc=2, max.nc=15, method="average")
par(opar)
table(nc$Best.n[1,])
barplot(table(nc$Best.n[1,]),
xlab="Numer of Clusters", ylab="Number of Criteria",
main="Number of Clusters Chosen by 26 Criteria")

Listing 16.3 - Obtaining the final cluster solution

clusters <- cutree(fit.average, k=5)
table(clusters)
aggregate(nutrient, by=list(cluster=clusters), median)
aggregate(as.data.frame(nutrient.scaled), by=list(cluster=clusters),
median)
plot(fit.average, hang=-1, cex=.8,
main="Average Linkage Clustering\n5 Cluster Solution")
rect.hclust(fit.average, k=5)

Plot function for within groups sum of squares by number of clusters

wssplot <- function(data, nc=15, seed=1234){
wss <- (nrow(data)-1)*sum(apply(data,2,var))
for (i in 2:nc){
set.seed(seed)
wss[i] <- sum(kmeans(data, centers=i)$withinss)}
plot(1:nc, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")}

Listing 16.4 - K-means clustering of wine data

data(wine, package="rattle")
head(wine)
df <- scale(wine[-1])
wssplot(df)
library(NbClust)
set.seed(1234)
nc <- NbClust(df, min.nc=2, max.nc=15, method="kmeans")
par(opar)
table(nc$Best.n[1,])
barplot(table(nc$Best.n[1,]),
xlab="Numer of Clusters", ylab="Number of Criteria",
main="Number of Clusters Chosen by 26 Criteria")
set.seed(1234)
fit.km <- kmeans(df, 3, nstart=25)
fit.km$size
fit.km$centers
aggregate(wine[-1], by=list(cluster=fit.km$cluster), mean)

evaluate clustering

ct.km <- table(wine$Type, fit.km$cluster)
ct.km
library(flexclust)
randIndex(ct.km)

Listing 16.5 - Partitioning around mediods for the wine data

library(cluster)
set.seed(1234)
fit.pam <- pam(wine[-1], k=3, stand=TRUE)
fit.pam$medoids
clusplot(fit.pam, main="Bivariate Cluster Plot")

evaluate clustering

ct.pam <- table(wine$Type, fit.pam$clustering)
ct.pam
randIndex(ct.pam)

Avoiding non-existent clusters

library(fMultivar)
set.seed(1234)
df <- rnorm2d(1000, rho=.5)
df <- as.data.frame(df)
plot(df, main="Bivariate Normal Distribution with rho=0.5")

wssplot(df)
library(NbClust)
nc <- NbClust(df, min.nc=2, max.nc=15, method="kmeans")
par(opar)
barplot(table(nc$Best.n[1,]),
xlab="Numer of Clusters", ylab="Number of Criteria",
main ="Number of Clusters Chosen by 26 Criteria")

library(ggplot2)
library(cluster)
fit <- pam(df, k=2)
df$clustering <- factor(fit$clustering)
ggplot(data=df, aes(x=V1, y=V2, color=clustering, shape=clustering)) +
geom_point() + ggtitle("Clustering of Bivariate Normal Data")

plot(nc$All.index[,4], type="o", ylab="CCC",
xlab="Number of clusters", col="blue")