Introduction
Explore the fundamentals of Agglomerative Hierarchical Clustering, a versatile clustering method that builds a hierarchy of clusters based on data similarity. Learn about its basic concepts, applications, and key advantages.
Theory
Dive deep into the theory behind Agglomerative Hierarchical Clustering, exploring its mathematical foundations, linkage criteria, and detailed examples to understand how this versatile clustering algorithm works.
Scikit-Learn Example
Learn how to implement Agglomerative Hierarchical Clustering using Scikit-Learn. This article includes a step-by-step guide and practical examples for clustering data with different linkage methods.
TensorFlow Example
Learn how to implement Agglomerative Hierarchical Clustering using TensorFlow. This article includes a detailed guide and practical examples for clustering data with custom distance metrics and linkage methods.
PyTorch Example
Learn how to implement Agglomerative Hierarchical Clustering using PyTorch. This article includes a detailed guide and practical examples for clustering data using PyTorch's tensor operations.
Common Mistakes and Best Practices
Learn about common pitfalls and best practices when implementing Agglomerative Hierarchical Clustering, including practical tips and example code to avoid errors and improve clustering results.
Comparison
A comprehensive comparison of Agglomerative Hierarchical Clustering with other popular unsupervised learning algorithms, highlighting the key differences, strengths, and best use cases.