Hierarchical Clustering Dendrogram | Data Science Tutorial

Introduction

In this lab, we will learn how to plot the corresponding dendrogram of a hierarchical clustering using AgglomerativeClustering and the dendrogram method available in scipy.

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Import the necessary libraries

We will start by importing the necessary libraries for this lab.

import numpy as np
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram
from sklearn.datasets import load_iris
from sklearn.cluster import AgglomerativeClustering

Load the dataset

We will use the load_iris() function from the sklearn.datasets module to load the iris dataset.

iris = load_iris()
X = iris.data

Create the model

Next, we will create the agglomerative clustering model using the AgglomerativeClustering() function from the sklearn.cluster module.

model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)

Fit the model

We will fit the agglomerative clustering model using the fit() method of the model object.

model = model.fit(X)

Plot the dendrogram

We will plot the dendrogram using the dendrogram() function from the scipy.cluster.hierarchy module and the plot_dendrogram() function defined in the original code.

plt.title("Hierarchical Clustering Dendrogram")
plot_dendrogram(model, truncate_mode="level", p=3)
plt.xlabel("Number of points in node (or index of point if no parenthesis).")
plt.show()

Summary

In this lab, we learned how to plot the corresponding dendrogram of a hierarchical clustering using AgglomerativeClustering and the dendrogram method available in scipy. We loaded the iris dataset, created an agglomerative clustering model, and fit the model. Finally, we plotted the dendrogram using the dendrogram() function from the scipy.cluster.hierarchy module and the plot_dendrogram() function defined in the original code.