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
Use TensorFlow features with maintained agglomerative clustering and verify linkage results without claiming native TensorFlow support.
PyTorch Example
Use PyTorch features with maintained agglomerative clustering and inspect distances while keeping custom tensor operations separate from a native estimator.
Common Mistakes and Best Practices
Avoid hierarchical-clustering mistakes involving scaling, metric-linkage compatibility, dendrograms, cuts, outliers, and validation.
Comparison
A comprehensive comparison of Agglomerative Hierarchical Clustering with other popular unsupervised learning algorithms, highlighting the key differences, strengths, and best use cases.