Particle Swarm Clustering Algorithm

This article focuses on the Particle Swarm Clustering Algorithm, a data clustering method based on swarm intelligence principles. The algorithm simulates collective behaviors observed in bird flocks or fish schools to achieve data clustering. It represents data points as particles and determines cluster assignments through inter-particle interactions. The algorithm features several key implementation components: particle position updates using velocity vectors, fitness evaluation for cluster quality assessment, and neighborhood best tracking for local optimization. Widely applied in domains including image processing, bioinformatics, and social network analysis, this algorithm offers advantages in simplicity, ease of implementation, and efficient handling of large-scale datasets. Through particle swarm clustering, we can better understand data structures and extract valuable patterns. The core implementation typically involves initializing particle positions randomly, calculating fitness using metrics like SSE (Sum of Squared Errors), and iteratively updating positions until convergence criteria are met.