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What is Hierarchical Clustering Explained about?
The document provides an overview of Hierarchical Clustering, an unsupervised machine learning algorithm that groups unlabeled datasets into clusters using a tree structure known as a dendrogram. It explains the two main approaches: Agglomerative (bottom-up) and Divisive (top-down), along with various distance and similarity measures used in clustering. Additionally, it discusses the advantages and disadvantages of hierarchical clustering, as well as Python implementation examples.
- Author
- Mayank Lovanshi
- Language
- EN