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

The paper "Clustering by Fast Search and Find of Density Peaks" published in Science (June 2014) presents a clustering algorithm based on the fundamental assumption that cluster centers are surrounded by neighbors with lower local density while maintaining relatively large distances from any points possessing higher density. This elegant algorithm requires calculating two key parameters for each data point: the local density value and the distance to points with higher density, both derived from pairwise distances between data points. The MATLAB implementation of this algorithm features several core components: a distance matrix computation module using pdist or custom distance functions, density calculation with configurable methods (Gaussian kernel-based or cutoff distance approach), and a decision graph visualization function that plots density versus distance to facilitate cluster center identification. This implementation supports various application domains including genomics for studying gene similarity patterns, social network analysis for identifying user relationships and enhancing recommendation systems, as well as image analysis and computer vision tasks for automated image recognition and classification. The code includes parameter optimization options for different dataset characteristics and cluster validation metrics to evaluate results.