Hierarchical Clustering | Divisive and Agglomerative Hierarchical Clustering | Intellipaat
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In this video, we will be focusing on one type of clustering: Hierarchical Clustering. We will explore what hierarchical clustering is, and how it works, and provide simple examples using data points to create a dendrogram. Additionally, we will delve into the two types of hierarchical clustering: Agglomerative clustering and Divisive clustering. Finally, we will implement it using the scikit-learn library. So, just stay tuned with us until the end.
🔵 Following topics are covered in this session:
00:00 - Introduction
01:15 - What is Hierarchical Clustering?
01:56 - How does hierarchical clustering work?
06:52 - Types of Hierarchical Clustering
10:55 - Implementation of Hierarchical Clustering
✅ What is meant by hierarchical clustering?
Hierarchical clustering is a method of organizing data into a tree-like structure based on the similarities between data points. It starts by considering each data point as a separate cluster and then progressively merges or divides clusters based on their similarity. The result is a hierarchy of clusters, visually represented as a dendrogram.
✅ What are the two types of hierarchical clustering?
Hierarchical clustering comprises two main types: Agglomerative clustering and Divisive clustering. Agglomerative clustering begins with individual data points as clusters and iteratively merges them based on similarity. Divisive clustering starts with all data points in one cluster, and then progressively divides them into smaller clusters.
✅ What is hierarchical clustering of features?
Hierarchical clustering of features involves organizing and grouping related features in a dataset based on their similarities and forming a dendrogram that visualizes the relationships within the dataset. This technique is particularly useful in machine learning and data analysis to identify patterns and relationships among different features.
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