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What is Hierarchical Clustering? An Introduction

This technique is frequently used to find patterns and correlations in large data sets in a variety of disciplines, including biology, social sciences, and computer science. 

Let’s dive deeper to understand the hierarchical clustering with the following sub-topics:

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Introduction to Hierarchical Clustering

In Data Science, a common method for clustering related objects is called hierarchical clustering. It is an unsupervised learning approach that may be used in exploratory data analysis because it doesn’t require any prior information about the data or labels. 

The data points are first represented as independent clusters in hierarchical clustering, and then they are combined or divided depending on some similarity or distance metric. Until a stopping condition is satisfied, usually when the desired number of clusters or a certain threshold for similarity is reached, this procedure is repeated.

A dendrogram, a tree-like structure that shows the hierarchical links between clusters, may be created using hierarchical clustering, which is one of its key advantages. Users may utilize the dendrogram to view the results of clustering and decide how many clusters to employ for future study.

Introduction to Hierarchical Clustering

In this dendrogram, the data points A, B, and C are shown at the bottom, and the clusters they belong to are represented by the branches above them. At each level, the distance or similarity between the clusters is shown on the vertical axis. In this example, the two closest clusters are A and B, which are merged into a new cluster, shown by the branch connecting them. This new cluster is then merged with C to form the final cluster, represented by the top branch in the dendrogram.

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Why do we need Hierarchical Clustering?

Hierarchical clustering is in demand because it is helpful in exploratory data analysis since it doesn’t require any prior information or labeling of the data. When working with vast and complicated datasets, this method may be very helpful since it enables researchers to find patterns and links in the data without any prior preconceptions.

It also offers a dendrogram as a visual representation of the grouping outcomes. Users may use the dendrogram to examine the data’s hierarchical structure and decide how many clusters to employ for future analysis. When working with huge datasets where the ideal number of clusters is not immediately obvious, this is very helpful. Hence this is the main reason why hierarchical clustering is in high demand.

How Hierarchical Clustering Works?

An unsupervised machine learning approach called hierarchical clustering is used to sort comparable items into groups based on their proximity or resemblance. It operates by splitting or merging clusters until a halting requirement is satisfied.

Each data point is first treated separately by the algorithm as a cluster. At each iteration after that, it merges the two closest clusters into a single cluster until only one cluster contains all of the data points. A dendrogram, which resembles a tree and depicts the hierarchical connection between the clusters, is the result of this procedure.

In hierarchical clustering, the choice of distance or similarity metric is crucial. Manhattan distance, Euclidean distance, and cosine similarity are three common distance metrics. The types of data and research issues are being addressed to determine the distance metric to be used.


!pip install scipy
import numpy as np
from scipy.cluster.hierarchy import dendrogram, linkage
import matplotlib.pyplot as plt
# Create a sample dataset
X = np.array([[5, 3], [10, 15], [15, 12], [24, 10], [30, 30], [85, 70], [71, 80], [60, 78], [70, 55], [80, 91]])
# Perform hierarchical clustering on the dataset
Z = linkage(X, 'ward')
# Plot the dendrogram
fig = plt.figure(figsize=(10, 5))
dn = dendrogram(Z)
Hierarchical Clustering Dendrogram

To show the process of hierarchical clustering, we generated a dataset X consisting of 10 data points with 2 dimensions. Then, the “ward” method is used from the SciPy library to perform hierarchical clustering on the dataset by calling the linkage function.

After that, the dendrogram function is used to plot the hierarchical clustering result, where the height of each node represents the distance between the merged clusters. The dendrogram plot provides an informative visualization of the clustering result.

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Types of Hierarchical Clustering

Agglomerative and divisive clustering are the two basic forms of hierarchical clustering. Let’s discuss each of them in detail:

Advantages of Hierarchical Clustering

Hierarchical clustering provides the following benefits:

Advantages of Hierarchical Clustering

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Use Cases of Hierarchical Clustering

A flexible and popular method with many useful applications is hierarchical clustering. Listed below are a few applications for hierarchical clustering:

Use Cases of Hierarchical Clustering

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Organizations may use hierarchical clustering as a potent tool for complicated data analysis and interpretation. Moreover, companies may use hierarchical clustering to find patterns, connections, and anomalies in data, which can help them make better decisions and achieve better business results.

If you have any queries related to this domain, then you can reach out to us at Intellipaat’s Data Science Community!

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