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Hierarchical Clustering

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Overview

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This introductory course explains hierarchical clustering for unsupervised learning, covering agglomerative and divisive methods, distance metrics, similarity measures, and techniques for determining the optimal number of clusters.

Syllabus

Introduction.
Agenda.
Introduction to Hierarchical Clustering.
Types of Hierarchical Clustering.
Euclidean Distance.
Manhattan Distance.
Minkowski Distance.
Jaccard Index.
Cosine Similarity.
Optimal Number of Clusters.
Summary.

Taught by

Great Learning

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