Introduction
Discover the basics of Affinity Propagation, a unique clustering algorithm that identifies exemplars among data points to form clusters without requiring the number of clusters to be specified in advance.
Theory
Explore the detailed theoretical foundation of Affinity Propagation, including the concepts of message passing, responsibility, availability, and the mathematical equations that drive this innovative clustering algorithm.
Scikit-learn Example
Learn how to implement Affinity Propagation using Scikit-learn, with a practical example to demonstrate clustering of real-world data.
TensorFlow Example
Learn how to implement Affinity Propagation using TensorFlow, with a practical example to demonstrate clustering of real-world data.
PyTorch Example
Learn how to implement Affinity Propagation using PyTorch, with a practical example to demonstrate clustering of real-world data.
Common Mistakes and Best Practices
Learn the common pitfalls and best practices when using Affinity Propagation for clustering, with practical tips and code examples to improve your results.
Comparison with Other Algorithms
A comprehensive comparison of Affinity Propagation with other popular unsupervised learning algorithms such as K-Means, DBSCAN, and Agglomerative Clustering, highlighting key differences, strengths, and best use cases.