Implementation and Applications of the Affinity Propagation Clustering Algorithm

Implementation and Applications of Affinity Propagation Clustering: Given a data matrix as input, the algorithm automatically determines the optimal number of clusters and assigns data points to appropriate categories based on similarity measures, without requiring pre-specification of cluster count.

Advanced Affinity Propagation Clustering Algorithm for Face Recognition with Demonstration Program

Latest AP clustering algorithm implementation and demo program based on the scientific paper "Affinity Propagation" published in Science, featuring efficient similarity matrix computation and cluster center selection.

MATLAB Implementation of Affinity Propagation Clustering Algorithm with Practical Examples

This resource provides a complete implementation of Affinity Propagation (AP) clustering algorithm in MATLAB, including working examples and detailed code explanations, making it an excellent learning tool for understanding and applying AP clustering techniques.

MATLAB Implementation of AP Clustering Algorithm

AP clustering algorithm implementation in MATLAB - an efficient clustering approach suitable for various data types with practical code examples