Agglomerative Clustering in Machine Learning

Agglomerative clustering is based on hierarchical clustering which is used to form a hierarchy of clusters. It is one of the types of clustering algorithms in machine learning. Unlike the K-Means and DBSCAN clustering algorithms, it is not very common but it is very efficient to form a hierarchy of clusters. If you’ve never used this algorithm before, this article is for you. In this article, I’ll give you an introduction to agglomerative clustering in machine learning and its implementation using Python.

Agglomerative Clustering

Clustering is a machine learning technique used to group similar instances together. This technique is used in unsupervised machine learning tasks where a dataset is not labelled and your task is to group similar instances. Clustering algorithms are mainly used in marketing campaigns where a business wants to find the most profitable customer group from its database of all customers.

Agglomerative clustering is one of the clustering algorithms where the process of grouping similar instances starts by creating multiple groups where each group contains one entity at the initial stage, then it finds the two most similar groups, merges them, repeats the process until it obtains a single group of the most similar instances. For example, think of bubbles floating on the water and getting attached, at the end, you will see a large group of bubbles.

Some of the advantages of using this algorithm for clustering are:

  1. It adapts very well to a large number of instances
  2. It can capture the clusters of different shapes
  3. It forms flexible and informative clusters
  4. It can also be used with any pairwise distance

I hope you now have understood what Agglomerative clustering is in machine learning. In the section below, I will take you through its implementation by using the Python programming language.

Agglomerative Clustering using Python

I will be using the scikit-learn library in Python to implement the agglomerative clustering algorithm. So let’s start by importing all the necessary Python libraries and the dataset we need to implement this algorithm:

   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)
0           1    Male   19                  15                      39
1           2    Male   21                  15                      81
2           3  Female   20                  16                       6
3           4  Female   23                  16                      77
4           5  Female   31                  17                      40

Here we can make clusters based on the income and spending of the customers. So let’s prepare the data accordingly:

   Income  Spending
0      15        39
1      15        81
2      16         6
3      16        77
4      17        40

Now below is how you can implement the Agglomerative clustering algorithm by using the scikit-learn library in Python:

Agglomerative Clustering

Summary

So this is how you can implement the Agglomerative Clustering algorithm by using the Python programming language. It is one of the clustering algorithms where the process of grouping similar instances starts by creating multiple groups where each group contains one entity at the initial stage, then it finds the two most similar groups, merges them, repeats the process until it obtains a single group of the most similar instances. I hope you liked this article on an introduction to Agglomerative Clustering in machine learning and its implementation using Python. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
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