What is Hierarchical Clustering?
Hierarchical clustering is a type of clustering algorithm that is used to group similar data points into clusters. It is a bottom-up approach that starts by treating each data point as a single cluster and then merges the closest pair of clusters until all the data points are grouped into a single cluster or a pre-defined number of clusters.
Hierarchical clustering can be divided into two types:
- Agglomerative Clustering
- Divisive Clustering
Agglomerative clustering is a bottom-up approach that starts by treating each data point as a single cluster and then merges the closest pair of clusters until all the data points are grouped into a single cluster or a pre-defined number of clusters. Divisive clustering is a top-down approach that starts by treating all the data points as a single cluster and then splits the cluster into smaller clusters until each cluster contains only one data point.
Agglomerative clustering with and without structure in Scikit Learn
Agglomerative clustering is a hierarchical clustering algorithm that is used to group similar data points into clusters. It is a bottom-up approach that starts by treating each data point as a single cluster and then merges the closest pair of clusters until all the data points are grouped into a single cluster or a pre-defined number of clusters.
In this blog, we will discuss how to perform agglomerative clustering in Scikit-Learn, a popular machine-learning library for Python. We will also discuss the differences between agglomerative clustering with and without structure.
Before diving into the details of agglomerative clustering in Scikit-Learn, let’s first understand the basics of hierarchical clustering and how it works.