Hierarchical Clustering Methods implementation in R: A Case Study

Today I will present the implementation of agglomerative hierarchical clustering in R. You can do a similar implementation in the language of your choice.

I will use the iris dataset here for explanation purpose. If you don’t have it in your R version you can download it from here

If you don’t have it in your R version you can download it from here. copy and save it to a text file called “iris.txt”. Please note, that there are no column headings to this dataset as of now. Now there are two ways to add the column headings.

Method 1: Open a new excel sheet and name the first row as sepalLength, sepalWidth, petalLength, petalWidth and after that you copy the dataset and paste it in excel sheet

Method 2: if you want to do it in R, you can use the function colnames() like
Now if you view the dataset like View(iris) you would see that it has the column names rather than the default V1, V2, V3, V4,V5. On the R console if you type >summary(iris) you will see that it has 149 values in 5 variables of which the first four are numeric and the last variable Species is categorical.

Now, you can install the cluster package using > install.packages(“cluster”) and after that load it using the library function like >library(cluster)

I would recommend you to take a look at the cluster package so as to know how to implement the various clustering algorithms in it. To check that use > ??cluster

I will now begin with the Agglomerative Clustering algorithm implementation in R first using the cluster package.

It is advisable to draw a random sample of data first otherwise the cluster dendogram will be messy because there are more than 100 values in 5 variables. To do this you can do like

my.dataframe[sample(nrow(dataset), size=), ] so for our example, the command will be


So now if you view the data like >View (iris.sample)

Random sample of iris dataset

you will see that it’s randomly sampled. Notice the column row.names it lists random row numbers. This proves that the data is randomly sampled.

Now to apply agglomerative hierarchical clustering I will use the agnes function of the cluster package.

Let me first briefly describe the agnes function parameters here;

agnes(x, diss = inherits(x, “dist”), metric = “euclidean”, stand = FALSE, method = “average”, par.method,keep.diss = n < 100, keep.data = !diss, trace.lev = 0) where x= data frame or data matrix or the dissimilarity function. In case of data matrix, all variables must be numeric. Missing Values (NA) are allowed. diss= logical flag if TRUE (which is the default value) then x is considered a dissimilarity matrix, if set FALSE then x is a matrix of observations by variable metric= euclidean distance or manhattan distance, stand= logical flag if TRUE (default value) then measurement in x are standardised before calculating the dissimilarity function. If x is already a dissimilarity matrix, then this argument will be ignored method= defines the clustering method to be used. There are 6 types, “single”, “complete”, “average”,”ward”,”weighted”,”flexible” Now to apply the agglomerative hierarchical clustering, I will use the agnes function of the cluster package
> iris.hc=agnes(iris.sample, diss=FALSE, metric="euclidean", stand="TRUE", method="average")

Note: I have kept the diss=FALSE because iris.sample is a dataframe and not a dissimilarity matrix. I have chosen euclidean distance metric. And the clustering method is single link. You can try others.

Now to plot this dendogram use the plot function like > plot(iris.hc)


You will notice that the dendogram has row numbers of the random sample which is visually not very interpretive. At least for me this dendogram is not visually interpretive. So I will use the labels command in the plot function to show the names like

> plot(iris.hc, labels=iris.sample$Species)


Clearly there are three clusters as shown in the plot.

Hope this helps.