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Hierarchical Clustering

4.52

Intermediate

1.5 Hrs

2K+

Ratings

Level

Learning hours

Learners

Skills you’ll Learn

Introduction to Hierarchical Clustering

Agglomerative Hierarchical Clustering

Euclidean Distance

Manhattan Distance

Minkowski Distance

Jaccard Index

About this course

Clustering is a very important part of machine learning that governs many applications that we have today. It is a concept that is widely used especially when we require the use of unsupervised learning techniques. Hierarchical clustering talks about how we could go on to pick up multiple data points and combine or separate them into individual clusters containing similar characteristics. Since it is very important for all you machine learning enthusiasts to understand this in detail, we here at Great Learning have come up with this course to help you get started with hierarchical clustering and to understand it completely.

Course Outline

Introduction to Hierarchical Clustering

Types of Hierarchical Clustering

Agglomerative Hierarchical Clustering

Euclidean and Manhattan Distance

Minkowski Distance and Jaccard Index

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Frequently Asked Questions

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Is this course free?

What is hierarchical clustering and how does it work?

Definition- Hierarchical clustering is also known as HCA or hierarchical cluster analysis. Hierarchical clustering is associated with data mining and statistics, which means that it is an algorithm that groups all the similar types of objects, and then it groups these objects, and is called clusters. Hierarchical cluster analysis is a method of cluster analysis in which a hierarchy of clusters is being built up. The best part of hierarchical clustering is the endpoint, as the endpoint is the set of clusters in which each cluster is different and distinct from each other, but the objects present inside these clusters are similar and same to each other.

Working of hierarchical clustering-

The best part of hierarchical clustering is the way it works. Hierarchical clustering first starts working by taking each observation as a distinct and separate cluster. After this step, it repeats the following steps:

Step 1- It identifies two clusters that are nearest and closest to each other.

Step 2- It operates by merging two similar kinds of clusters.

This process keeps on going until all the clusters are merged together.

What is hierarchical clustering used for?

The hierarchical clustering method is mainly used for analyzing the social network of the data. In this method, clusters are compared with each other based on similarity.

How do you interpret hierarchical clustering?

The main point of interpretation of hierarchical clustering is to analyze the clusters and then join them together.