TY - JFULL AU - Neveen I. Ghali and Nahed El-Dessouki and Mervat A. N. and Lamiaa Bakrawi PY - 2008/7/ TI - Exponential Particle Swarm Optimization Approach for Improving Data Clustering T2 - International Journal of Computer and Information Engineering SP - 1817 EP - 1822 VL - 2 SN - 1307-6892 UR - https://publications.waset.org/pdf/8531 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 18, 2008 N2 - In this paper we use exponential particle swarm optimization (EPSO) to cluster data. Then we compare between (EPSO) clustering algorithm which depends on exponential variation for the inertia weight and particle swarm optimization (PSO) clustering algorithm which depends on linear inertia weight. This comparison is evaluated on five data sets. The experimental results show that EPSO clustering algorithm increases the possibility to find the optimal positions as it decrease the number of failure. Also show that (EPSO) clustering algorithm has a smaller quantization error than (PSO) clustering algorithm, i.e. (EPSO) clustering algorithm more accurate than (PSO) clustering algorithm. ER -