Welcome to the session on ‘Hierarchical Clustering’. In the previous sessions, you got a basic understanding of what clustering is and how you can use the K-Means algorithm to create clusters in your data set. You also saw the execution of the K-Means algorithm in python.
In this session
You will learn about another algorithm to achieve unsupervised clustering. This is called Hierarchical Clustering. Here, instead of pre-defining the number of clusters, you first have to visually describe the similarity or dissimilarity between the different data points and then decide the appropriate number of clusters on the basis of these similarities or dissimilarities.
You will learn about:
- Hierarchical clustering algorithm.
- Interpreting the dendrogram.
- Cutting the dendrogram.
- Types of linkages.
Guidelines for in-module questions
The in-video and in-content questions for this module are not graded. The graded questions are given in a separate segment at the end of the session. The questions in that segment will adhere to the following guidelines:
First Attempt Marks | Second Attempt Marks | |
Question with 2 Attempts | 10 | 5 |
Question with 1 Attempt | 10 | 0 |
People you will hear from in this session
Subject Matter Expert
Assistant Professor, IIIT- B
The International Institute of Information Technology, Bangalore, also known as IIIT-B, is one of India’s foremost graduate schools. Through its Integrated M.Tech., M.Tech., M.S. (Research) and PhD programs in the IT space, it focuses equally on innovation and education.
Head Analytics and AVP Strategy, Viacom18
Presenter
Rohit Sharma