Exponential Particle Swarm Optimization Approach for Improving Data Clustering
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.
[1] Cui, X., Potok, T., Palathingal, P., Document Clustering using Particle
Swarm Optimization, Swarm Intelligence Symposium, 2005.
Proceedings 2005 IEEE, pp. 185- 191
[2] Cui, X., Potok T., Document Clustering Analysis based on Hybrid
PSO+K-means Algorithm, Journal of Computer Sciences (special issue),
pp. 27-33, 2005.
[3] Falco, I., Cioppa, A., Tarantino, E., Facing Classification Problems with
Particle Swarm Optimization, Applied Soft Computing, Vol.7, pp. 652-
658, 2007
[4] Jain, A., Murty, M., Flynn, P., Data Clustering: A Review, ACM
Computing Surveys, Vol. 31, No. 3, 1999.
[5] Kao, Y. -T. et al., A Hybridized Approach to Data Clustering, Expert
systems and applications (2007), doi: 10.1016/j.eswa.2007.01.028
[6] Kennedy, J., Eberhart, R., Particle Swarm Optimization, proceedings of
the IEEE International joint conference or Neural networks, vol.4, pp.
1942-1948, 1995.
[7] Li-ping, Z., Huan-jun, Y., Shang-xu, H., Optimal Choice of Parameters
for Particle Swarm Optimization, Journal of Zhejiang University
Science, Vol. 6(A)6, pp.528-534, 2004.
[8] Merwe DW., Engelbrecht AP., Data Clustering using Particle Swarm
Optimization, IEEE Congress on Evolutionary Computation, Canberra,
Australia, 215-220, 2003
[9] Shi, Y., Eberhart, R., Parameter Selection in Particle Swarm
Optimization, proceedings of the 7th International Conference on
Evolutionary Programming VII, pp. 591 - 600, 1998.
[10] Sousa, T., Silva, A., Neves, A., Particle Swarm Based Data Mining
Algorithms for Classification Tasks, Parallel Computing 30, pp. 767-
783, 2004.
[11] El-Desouky N., Ghali N., Zaki M., A New Approach to Weight Variation
in Swarm Optimization, proceedings of Al-azhar Engineering, the 9th
International Conference, April 12 - 14, 2007.
[1] Cui, X., Potok, T., Palathingal, P., Document Clustering using Particle
Swarm Optimization, Swarm Intelligence Symposium, 2005.
Proceedings 2005 IEEE, pp. 185- 191
[2] Cui, X., Potok T., Document Clustering Analysis based on Hybrid
PSO+K-means Algorithm, Journal of Computer Sciences (special issue),
pp. 27-33, 2005.
[3] Falco, I., Cioppa, A., Tarantino, E., Facing Classification Problems with
Particle Swarm Optimization, Applied Soft Computing, Vol.7, pp. 652-
658, 2007
[4] Jain, A., Murty, M., Flynn, P., Data Clustering: A Review, ACM
Computing Surveys, Vol. 31, No. 3, 1999.
[5] Kao, Y. -T. et al., A Hybridized Approach to Data Clustering, Expert
systems and applications (2007), doi: 10.1016/j.eswa.2007.01.028
[6] Kennedy, J., Eberhart, R., Particle Swarm Optimization, proceedings of
the IEEE International joint conference or Neural networks, vol.4, pp.
1942-1948, 1995.
[7] Li-ping, Z., Huan-jun, Y., Shang-xu, H., Optimal Choice of Parameters
for Particle Swarm Optimization, Journal of Zhejiang University
Science, Vol. 6(A)6, pp.528-534, 2004.
[8] Merwe DW., Engelbrecht AP., Data Clustering using Particle Swarm
Optimization, IEEE Congress on Evolutionary Computation, Canberra,
Australia, 215-220, 2003
[9] Shi, Y., Eberhart, R., Parameter Selection in Particle Swarm
Optimization, proceedings of the 7th International Conference on
Evolutionary Programming VII, pp. 591 - 600, 1998.
[10] Sousa, T., Silva, A., Neves, A., Particle Swarm Based Data Mining
Algorithms for Classification Tasks, Parallel Computing 30, pp. 767-
783, 2004.
[11] El-Desouky N., Ghali N., Zaki M., A New Approach to Weight Variation
in Swarm Optimization, proceedings of Al-azhar Engineering, the 9th
International Conference, April 12 - 14, 2007.
@article{"International Journal of Information, Control and Computer Sciences:57608", author = "Neveen I. Ghali and Nahed El-Dessouki and Mervat A. N. and Lamiaa Bakrawi", title = "Exponential Particle Swarm Optimization Approach for Improving Data Clustering", abstract = "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.", keywords = "Particle swarm optimization, data clustering,exponential PSO.", volume = "2", number = "6", pages = "2015-5", }