As a data scientist, you might have come across the term “dendrogram” while working with clustering algorithms. Dendrograms are an essential tool in hierarchical clustering, which is a popular technique for grouping similar data points together.

What is a Dendrogram?

A dendrogram is a tree-like diagram that represents the arrangement of clusters produced by a hierarchical clustering algorithm. The diagram starts with a single cluster that contains all the data points. It then recursively splits the cluster into smaller sub-clusters until each sub-cluster contains only one data point. The dendrogram displays the order in which these splits were made and the distance between the clusters at each step.

How is a Dendrogram Used?

Dendrograms are primarily used to visualize the results of hierarchical clustering algorithms. They allow us to see the structure of the data and how the clusters are related to each other. Dendrograms can also help us determine the optimal number of clusters to use in our analysis. We can do this by looking at the height of the branches in the dendrogram. The height represents the distance between the clusters, and the optimal number of clusters is where the height changes the most.

Benefits of Using a Dendrogram

Visual representation: Dendrograms provide a visual representation of the clustering results, making it easier to understand and interpret the data. Optimal number of clusters: Dendrograms can help us determine the optimal number of clusters to use in our analysis, which can improve the accuracy of our results. Comparing clustering algorithms: Dendrograms can be used to compare the results of different clustering algorithms and select the one that works best for our data.

Introduction to Hierarchical Clustering How to Interpret a Dendrogram Scikit-learn Documentation on Hierarchical Clustering