Scikitlearn is a popular machine learning library used to develop predictive models. One of the clustering methods that can be implemented using scikitlearn is agglomerative clustering. Agglomerative clustering is a hierarchical clustering technique that involves merging individual data points into larger clusters based on how similar they are to each other. In this approach, the most similar data points are merged first, followed by less similar ones until all points are in a single cluster. This can be a useful method for exploring data patterns and identifying relationships between variables.
Understanding Clustering
Clustering is an unsupervised machine learning technique that allows us to find patterns and groupings in data without using predefined categories. It is a process of partitioning a set of data points into subsets or clusters based on their similarity. Clustering algorithms can be broadly classified into two categories: hierarchical and nonhierarchical. In this article, we will focus on agglomerative clustering, a type of hierarchical clustering algorithm, and how it is implemented using Scikitlearn.
Hierarchical Clustering
Hierarchical clustering algorithms group data points into a treelike structure, where each node represents a cluster of data points. There are two types of hierarchical clustering, agglomerative and divisive. Agglomerative clustering is a bottomup approach, where each data point starts as a separate cluster, and the algorithm successively merges the two closest clusters until all the data points belong to a single cluster. Divisive clustering, on the other hand, is a topdown approach, where all the data points start as a single cluster, and the algorithm successively splits the cluster into smaller clusters until each cluster contains only one data point.
Agglomerative Clustering
Agglomerative clustering is a commonly used hierarchical clustering algorithm that works by iteratively merging the two closest clusters into a single larger cluster. The algorithm starts by assigning each data point to its own cluster and computes the pairwise distance between all the clusters. The two closest clusters are then merged, and the process is repeated until all the data points belong to a single cluster.
Agglomerative clustering can be visualized using a dendrogram, a treelike diagram that shows the hierarchical relationships between the clusters. Each point on the dendrogram represents a cluster, and the height of the point represents the distance between the clusters.
ScikitLearn and Agglomerative Clustering
Scikitlearn is a popular Python library for machine learning and data analysis. It provides a wide range of tools for clustering, including agglomerative clustering. Scikitlearn’s implementation of agglomerative clustering allows us to specify the linkage criterion and the number of clusters we want to identify.
Linkage Criteria
The linkage criterion determines the distance between two clusters. Scikitlearn provides several linkage criteria, including:
 Ward Linkage: Minimizes the variance of the clusters being merged.
 Complete Linkage: Maximizes the distance between the closest points of the clusters being merged.
 Average Linkage: Takes the average of the distances between all pairs of points in the two clusters being merged.
 Single Linkage: Minimizes the distance between the closest points of the clusters being merged.
Number of Clusters
The number of clusters we want to identify is a hyperparameter that needs to be specified before running the clustering algorithm. Scikitlearn provides several methods for determining the optimal number of clusters, including the elbow method and silhouette analysis.
Implementation and Examples
Let’s look at an example of how to implement agglomerative clustering using Scikitlearn. We will use the Iris dataset, which contains measurements of the sepal length, sepal width, petal length, and petal width for three different species of iris flowers.
“`python
X = iris.data
“`
In this example, we specify that we want to identify three clusters and use the Ward linkage criterion. We then fit the agglomerative clustering algorithm to the Iris dataset and print the resulting cluster labels.
Number of Clusters in Depth
The number of clusters is another critical parameter in agglomerative clustering. It determines how many clusters the algorithm should identify. The optimal number of clusters depends on the specific problem we are trying to solve. Scikitlearn provides several methods for determining the optimal number of clusters.

Elbow method: The elbow method is a heuristic method for determining the optimal number of clusters. It involves plotting the sum of squared distances between the observations and their closest cluster center for different values of k (the number of clusters). The optimal number of clusters is the value of k where the curve starts to flatten out and form an elbow.

Silhouette analysis: Silhouette analysis is a more sophisticated method for determining the optimal number of clusters. It involves computing the silhouette score for each observation, which measures how similar an observation is to its own cluster compared to other clusters. The silhouette score ranges from 1 to 1, with higher values indicating better cluster quality. The optimal number of clusters is the value of k that maximizes the average silhouette score across all observations.
Implementation and Examples in Depth
Let’s look at an example of how to implement agglomerative clustering using Scikitlearn in more detail. We will use the Iris dataset, which contains measurements of the sepal length, sepal width, petal length, and petal width for three different species of iris flowers.
In this example, we load the Iris dataset using Scikitlearn’s load_iris()
function. We then extract the data matrix X
from the dataset. We create an instance of the AgglomerativeClustering
class with n_clusters=3
and linkage='ward'
. We then fit the agglomerative clustering algorithm to the Iris dataset using the fit()
method. Finally, we print the resulting cluster labels using the labels_
attribute.
FAQs for scikit learn agglomerative clustering
What is agglomerative clustering with regards to machine learning?
Agglomerative clustering is a machine learning technique used for data analysis. It involves the creation of multiple groups that are similar in nature and can be formed via the bottomup approach in hierarchical clustering. In this technique, the process of clustering begins by treating every observation as a separate cluster, and then they are merged according to a particular criterion until a specific number of clusters is achieved.
What is scikit learn?
Scikit learn is a free software machine learning library for the Python programming language. It provides powerful algorithms that enable developers to perform a range of tasks, including regression, classification, clustering, and more. It also provides various tools for data analysis and visualization.
What is scikit learn agglomerative clustering?
Scikit learn agglomerative clustering is a module in scikit learn library that implements hierarchical agglomerative clustering. It provides a variety of linkage options and is useful for identifying structure within a dataset that may not be immediately apparent.
What is hierarchical clustering?
Hierarchical clustering is a technique in data mining and machine learning that involves grouping similar data points together. It involves creating a hierarchy of clusters, starting with each individual data point and gradually merging them into larger clusters.
What are the advantages of using scikit learn agglomerative clustering?
Scikit learn agglomerative clustering offers a variety of advantages. It is scalable, meaning it can handle large datasets, and it is easy to use and understand. Additionally, it allows for the selection of different types of linkage methods, which provides greater flexibility in creating clusters that suit specific requirements.
How do you use scikit learn agglomerative clustering?
To use scikit learn agglomerative clustering, first import the module and the dataset you want to cluster. Then, specify the number of clusters you want to create and the linkage method to use. Finally, fit the model to the data and retrieve the cluster assignments.