Understanding the Concept of Hierarchical Clustering in Data Analysis: Functions, Types & Steps

Clustering refers to the grouping of similar data in groups or clusters in data analysis. These clusters help data analysts organise similar data points into one group while also differentiating them from other data that are not similar. 

Hierarchical clustering of data is one of the methods used to group data into a tree of clusters. It is one of the most popular and useful approaches to data grouping. If you want to be a part of the growing field of data science and data analysis, hierarchical clustering is one of the most important things to learn.

This article will help you understand the nature of hierarchical clustering, its function, types and advantages. 

What is Hierarchical Clustering?

As the name suggests, hierarchical clustering groups different data into clusters in a hierarchical or tree format. Every data point is treated as a separate cluster in this method. Hierarchical cluster analysis is very popular amongst data scientists and data analysts as it summarises the data into a manageable hierarchy of clusters that is easier to analyse. 

The hierarchical clustering algorithms take multiple different data points and take the closest of the two to make a cluster. It repeats these steps until all the data points turn into one cluster. The process can also be inverted to divide one single merged cluster into different smaller clusters and ultimately into data points. 

The hierarchical method of clustering can be visually represented as a dendrogram which is a tree-like diagram. A dendrogram can be cut off at any point during the clustering process when the desired number of clusters has been made. This also makes the process of analysing the data easier. 

How does Hierarchical Clustering work?

The process of hierarchical clustering is quite simple to understand. A hierarchical clustering algorithm treats all available data sets as different clusters. Then, it identifies two data sets that are the most similar and merges them into a cluster. After that, the system keeps repeating these steps until all the data points merge into one large cluster. The process can also be stopped once the required number of clusters is available for analysis. 

The progress and output of a hierarchical clustering process can be visualised as a dendrogram that can help you identify the relationship between different clusters and how similar or different they are in nature. 

Types of Hierarchical Clustering

A hierarchical clustering algorithm can be used in two different ways. Here are the characteristics of two types of hierarchical clustering that you can use. 

1. Agglomerative Hierarchical Clustering 

The agglomerative method is the more popularly used way of hierarchically clustering data. In this method, the algorithm is presented with multiple different data sets, each of which is treated as a cluster of its own. Then the algorithm starts combining into clusters of twos based on how similar they are to each other. It repeats these steps until the required number of clusters is reached. This method is more popularly used in hierarchical cluster analysis

2. Divisive Hierarchical Clustering 

The divisive method of hierarchical clustering is the reverse of the agglomerative method. In this method, the algorithm is presented with a single large cluster of numerous data points which it differentiates step by step based on their disparity. This results in multiple data sets that have different properties. The divisive method is not used often in practice. 

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Steps in Hierarchical Clustering

As mentioned before, there are three main steps in the hierarchical clustering of data. 

  1. The identification of similarities between two different data points. 
  2. Merging them into one cluster. 
  3. Repeating these steps for all data points until they are merged into one large cluster of data. 

However, it is also very important to remember how to identify similar points in hierarchical clustering. If you study a dendrogram produced by an algorithm, you can easily identify the central points of each different cluster. The clusters that have the least distance from each other in the dendrogram are the most similar. This is the reason why it’s also referred to as a distance-based algorithm. The similarity between one cluster and all the other ones in a dendrogram is called a proximity matrix. 

You also have to choose the correct distance measure while using hierarchical clustering. For example, based on whether you choose your distance measure to be their gender or educational background, a data set involving information about the same people will produce different dendrograms. 

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Hierarchical Clustering Python

Now that you have a clear understanding of hierarchical clustering, let us look at how to perform hierarchical clustering Python. Here is what performing hierarchical clustering would look like using Python’s ‘scikit-learn’ library. 

Let us suppose that there are two variables (x and y) in a dataset with six observations: 

Observations  x y
1 1 1
2 2 1
3 4 3
4 5 4
5 6 5
6 7 5

As a scatter plot, this is how these observations will be visualised: 


import numpy as


import matplotlib.pyplot as plt

# Define the dataset

X = np.array([[1, 1], [2, 1], [4, 3], [5, 4], [6, 5], [7, 5]])

# Plot the data

plt.scatter(X[:,0], X[:,1])

There are two clusters of observations in this plot- one includes lower values of x and y, and the other with higher values of x and y

You can use ‘scikit learn’ to perform hierarchical clustering on this dataset.

The two clusters of observations in the plot have different values. One consists of higher values of x and y, and the other with lower. 

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Out of the two main methods of hierarchical clustering that we have discussed before, we will use the agglomerative clustering method with the ‘ward’ linkage method. The ‘ward’ method minimises the variations of the clusters which are being merged together, therefore producing clusters which are similar in size and shape. 

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from sklearn.cluster import AgglomerativeClustering

# Perform hierarchical clustering

clustering AgglomerativeClustering (n_clusters=2, linkage=’ward‘).fit(X)

The ‘n-clusters’ parameter was used here to specify that we want two clusters. 

We can use different colours for each cluster when we plot them:


# Plot the clusters

colors= np.array([‘r‘, ‘b‘]) 

plt.scatter (X[:,0], X[:,1], c=colors [clustering.labels_])

The two clusters in the data have been correctly identified by the clustering algorithm. You can also use what label the clustering algorithm has assigned to each observation: 




[0 0 1 1 1 1]

The last four observations were assigned to cluster 1, while the first two were assigned to cluster 0. 

If you want to visualise the hierarchical structure of these clusters, you can generate a dendrogram to do so: 


from scipy.cluster.hierarchy import dendrogram, linkage

# Compute the linkage matrix

Z = linkage(X, ‘ward‘)

# Plot the dendrogram


The dendrogram can help us visualise the hierarchy of merged clusters. 

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Q. Why do we do hierarchical clustering in data science?

Hierarchical clustering is used to group data based on various similar attributes. Distributing data aspects in visually comprehensible groups simplifies its practical implementation by easily looking at the dendrogram.

Q. What is hierarchical clustering used in?

Hierarchical clustering is a widely used form of grouping data generated through social networking sites. Using this data, analysts can reap valuable insights relevant to enhance their business processes and generative improve revenue.

Q. What are the limitations of hierarchical clustering?

Hierarchical clustering does not suit mixed types or missing data. Another limitation of hierarchical clustering is it does not perform well with an extensively large set of data.

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