Affinity Propagation Self-Similarity Spreading Clustering Source Code

This repository provides the complete source code for Affinity Propagation clustering algorithm! Affinity Propagation is an advanced clustering technique that automatically determines the number of clusters based on pairwise similarities between data points. The algorithm operates by calculating a similarity matrix that represents the affinity between all data point pairs, then iteratively exchanges real-valued messages between points until a high-quality set of exemplars (cluster centers) and corresponding clusters emerge. The source code implementation includes core components such as: - Similarity matrix computation using negative squared Euclidean distances - Responsibility update function handling "competition" between potential exemplars - Availability update function managing "support" evidence for exemplar candidacy - Convergence detection mechanism with damping factor for stability This implementation helps researchers and developers understand the algorithm's intricate details, including message passing mechanics and exemplar selection criteria. The code is structured to allow easy modification of similarity metrics, damping factors, and convergence thresholds. For those interested in clustering algorithms and similarity propagation techniques, this well-documented source code serves as an excellent educational resource and practical foundation for further optimization and customization. The implementation follows the original Affinity Propagation paper's methodology while providing clear variable naming and commented sections explaining each computational step, making it suitable for both learning and production deployment scenarios.