## 1.1. Data preparation and initial vizualization

Let us first clean memory and setup the current folder, where our results will be stored.

rm(list=ls()) ## clean memory
gc() ## garbage collection

getwd()   # shows current folder
setwd("D:/Data/Clustering") # sets the current folder

Here we will use the standard iris dataset (R.Fisher, 1936) that is available in R by default.

str(iris)
## 'data.frame':    150 obs. of  5 variables:
##  $Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... ##$ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... ##$ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... species = iris$Species
color = as.integer(species)    ## for the moment let's color by species
point = 14+as.integer(species) ## point shape will represent species
plot(iris[,-5],pch=point, col=color) ## let's extract features and scale the data
X = as.matrix(iris[,-5])
X[,] = scale(X)

Now, apply PCA to vizualize 4d data in 2d. This is the best possible representation based on variability.

PC = prcomp(X)
plot(PC$x[,1],PC$x[,2],col=color,pch=point)
legend("topright",legend=levels(species), pch=unique(point)) ## 1.2. Hierarchical clustering

### 1.2.1. Simple hierarchical clustering

H = hclust(dist(X))
cl = cutree(H,k=3)

plot(H,labels=species,hang=-1,cex=0.75)
points(x=1:nrow(X),y=rep(0,nrow(X)),pch=point[H$order],col=cl[H$order]) table(cl, species)
##    species
## cl  setosa versicolor virginica
##   1     49          0         0
##   2      1         21         2
##   3      0         29        48

Now let’s check the stability of such clustering by removing one flower from each group

idx = c(-41,-98,-144) ## 3 preselected objects removed
X1 = X[idx,]
color1 = color[idx]
point1 = point[idx]
species1 = species[idx]
H1 = hclust(dist(X1))
cl1 = cutree(H1,k=3)
plot(H1,labels=species1,hang=-1,cex=0.75, main="Cluster Dendrogram after Removing")
points(x=1:nrow(X1),y=rep(0,nrow(X1)),pch=point1[H1$order],col=cl1[H1$order]) table(cl[idx],cl1)  ## compare with previous clustering
##    cl1
##      1  2  3
##   1 41  7  0
##   2 24  0  0
##   3 27  0 48
table(species1,cl1) ## compare with species groups
##             cl1
## species1      1  2  3
##   setosa     42  7  0
##   versicolor 36  0 13
##   virginica  14  0 35

You can easily build bi-clusters of the dataset (clusteting similar objects and similar features at the same time) using heatmap() function, or more advanced pheatmap(). Please use ?pheatmap to see what visual parameters you can control.

library(pheatmap)
pheatmap(X) ### 1.2.2. Other distance measures

Try using other distance measures that are implemented in dist() function:

H2 = hclust(dist(X,method="maximum"))
cl2 = cutree(H2,k=3)
table(cl2, species)
##    species
## cl2 setosa versicolor virginica
##   1     37         40        20
##   2     13          0         0
##   3      0         10        30

Or use your own definition of the distance, e.g. often-used correlation measure:

D = (1-cor(t(X)))/2
D = as.dist(D)
H3 = hclust(D)
cl3 = cutree(H3,k=3)
table(cl3, species)
##    species
## cl3 setosa versicolor virginica
##   1     49          2         0
##   2      1         10        14
##   3      0         38        36

### 1.2.3 Consensus hierarchical clustering

Consensus HC can improve stability of clustering. Usually it is performed in 3 steps:

1. Resampling of the original set
2. Clustering
3. Summarizing the results

However, there is no guarantee that you will get what you expect…

library(ConsensusClusterPlus)
results = ConsensusClusterPlus(t(X),maxK=6,reps=50,pItem=0.8,pFeature=1,title="IRIS ConClust",clusterAlg="hc",distance="euclidean",seed=12345,plot="png")
## end fraction
## clustered
## clustered
## clustered
## clustered
## clustered
plot(PC$x[,1],PC$x[,2],col=results[]$consensusClass, pch=point) legend("topright",legend=levels(species), pch=unique(point)) table(species, results[]$consensusClass)
##
## species       1  2  3
##   setosa     50  0  0
##   versicolor  0 50  0
##   virginica   0 47  3

## 1.3. Non-hierarchical clustering

### 1.3.1. k-means

Widely used method of non-hierarchical clustering is k-means. You should predefine number of clusters

cl = kmeans(X,centers=3, nstart=10)$cluster plot(PC$x[,1],PC$x[,2],col = cl,pch=point) K-means is usually more stable to object removal than hierarchical methods. cl1 = kmeans(X1,centers=3,nstart=10)$cluster
plot(PC$x[idx,1],PC$x[idx,2],col = cl1,pch=point1) table(cl[idx],cl1)
##    cl1
##      1  2  3
##   1  0  0 49
##   2  0 46  0
##   3 51  1  0

### 1.3.2. Partitioning around medoids (PAM)

This method helps reducing effects of the outliers.

library(cluster)
cl = pam(X,k=3)$cluster plot(PC$x[,1],PC$x[,2],col = cl,pch=point) ## stability to object removal ? cl1 = pam(X1,k=3)$cluster
table(cl[idx],cl1)
##    cl1
##      1  2  3
##   1 49  0  0
##   2  0 44  0
##   3  0 11 43

## 1.4. Optimal number of clusters

Unfortunately, there is no panacea. Try several methods and select the most reasonable and defendable result :)

You can look at within cluster variability (should be minimized):

wss=NULL

for (k in 1:10)
wss[k] = kmeans(X,centers=k)$tot.withinss plot(1:10,wss,lwd=2,type="b",pch=19,xlab="Number of clusters",ylab="Total Within-cluster SS",col=4,main="WSS vs number of clusters") Here 2 or 3 clusters look reasonably good (at least much better than 1 cluster). Futher increase of clusters does not reduce WSS much. You can use some specialized packages to make deeper investigation: library(factoextra) ## Warning: package 'factoextra' was built under R version 3.4.4 ## Loading required package: ggplot2 ## Welcome! Related Books: Practical Guide To Cluster Analysis in R at https://goo.gl/13EFCZ library(NbClust) fviz_nbclust(X, kmeans, method = "silhouette") fviz_nbclust(X, kmeans, method = "wss") fviz_nbclust(X, kmeans, method = "gap_stat") ## 1.5. Density-based clustering Density-based clustering allows extracting continuous clusters of complex shapes, that are not detected by standard distance-based methods. In bioinformatics, it is often used to determine the groups of cells in cytometry or single-cell genomics. The main parameter is epsilon - the radius of the “neighboring area”. library(dbscan) ## Warning: package 'dbscan' was built under R version 3.4.4 res = dbscan(X,eps=0.5,minPts=5) res ## DBSCAN clustering for 150 objects. ## Parameters: eps = 0.5, minPts = 5 ## The clustering contains 2 cluster(s) and 34 noise points. ## ## 0 1 2 ## 34 45 71 ## ## Available fields: cluster, eps, minPts plot(PC$x[,1],PC$x[,2],col = 1+res$cluster,pch=point) res = dbscan(X,eps=1.5,minPts=5)
plot(PC$x[,1],PC$x[,2],col = 1+res\$cluster,pch=point) 