MATLAB Implementation of "Clustering by Fast Search and Find of Density Peaks"

The paper "Clustering by Fast Search and Find of Density Peaks," published in Science in June 2014, proposes a clustering algorithm based on the hypothesis that cluster centers are surrounded by neighbors with lower local density and maintain relatively large distances from any points with higher density. This elegant algorithm requires computing two key metrics for each data point: its local density and the distance to the nearest point with higher density, both derived from pairwise distances between data points. The MATLAB implementation uses efficient distance matrix calculations and density estimation techniques to identify cluster centers automatically. The MATLAB implementation of this algorithm has diverse applications. In biology, it can analyze genomic data to study gene similarities. For social network analysis, it identifies user relationships and enables applications in social media recommendation systems. Additionally, the algorithm supports image analysis and computer vision tasks by automatically recognizing and categorizing visual patterns. The code includes functions for distance computation, density peak detection, and cluster assignment, making it adaptable to various data types and scales.