{
"title": "Exponential Particle Swarm Optimization Approach for Improving Data Clustering",
"authors": "Neveen I. Ghali, Nahed El-Dessouki, Mervat A. N., Lamiaa Bakrawi",
"country": null,
"institution": null,
"volume": "18",
"journal": "International Journal of Computer, Electrical, Automation, Control and Information Engineering",
"pagesStart": 1818,
"pagesEnd": 1823,
"ISSN": "1307-6892",
"URL": "http:\/\/waset.org\/publications\/8531",
"abstract": "In this paper we use exponential particle swarm\r\noptimization (EPSO) to cluster data. Then we compare between\r\n(EPSO) clustering algorithm which depends on exponential variation\r\nfor the inertia weight and particle swarm optimization (PSO)\r\nclustering algorithm which depends on linear inertia weight. This\r\ncomparison is evaluated on five data sets. The experimental results\r\nshow that EPSO clustering algorithm increases the possibility to find\r\nthe optimal positions as it decrease the number of failure. Also show\r\nthat (EPSO) clustering algorithm has a smaller quantization error\r\nthan (PSO) clustering algorithm, i.e. (EPSO) clustering algorithm\r\nmore accurate than (PSO) clustering algorithm.",
"references": null,
"publisher": "World Academy of Science, Engineering and Technology",
"index": "International Science Index 18, 2008"
}