TY - JFULL AU - Mahamed G.H. Omran and Andries P Engelbrecht and Ayed Salman PY - 2007/10/ TI - Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classification T2 - International Journal of Computer and Information Engineering SP - 2897 EP - 2903 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/11937 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 9, 2007 N2 - A new dynamic clustering approach (DCPSO), based on Particle Swarm Optimization, is proposed. This approach is applied to unsupervised image classification. The proposed approach automatically determines the "optimum" number of clusters and simultaneously clusters the data set with minimal user interference. The algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions. Using binary particle swarm optimization the "best" number of clusters is selected. The centers of the chosen clusters is then refined via the Kmeans clustering algorithm. The experiments conducted show that the proposed approach generally found the "optimum" number of clusters on the tested images. ER -