Clustering by Passing Messages Between Data Points with Code Implementation

The 2007 Science journal paper "Clustering by passing messages between data points" introduced the Affinity Propagation clustering algorithm, which operates by exchanging messages between data points. Below you can find the Python implementation of this algorithm: The Affinity Propagation algorithm implementation typically involves two main message types: responsibility and availability messages. Responsibility messages indicate how well-suited a point is to serve as an exemplar for another point, while availability messages reflect how appropriate it would be for a point to choose another point as its exemplar. Python implementation structure: def clustering_by_passing_messages(data): # Initialize similarity matrix and message arrays # Iteratively update responsibility and availability matrices # Identify exemplars and assign data points to clusters # Convergence typically occurs when exemplars remain unchanged return clusters Example usage: data = [...] # Input data points as array or matrix clusters = clustering_by_passing_messages(data) Affinity Propagation is an efficient clustering algorithm particularly effective for large-scale datasets. Through message passing between data points, the algorithm autonomously discovers cluster structures and groups data points into distinct clusters. This method requires no pre-specification of cluster numbers and automatically determines the number of clusters based on data characteristics. The algorithm finds wide applications in data analysis, image processing, social network analysis, bioinformatics, and various pattern recognition domains where it excels at identifying representative exemplars within datasets.