Abstract
New variant of PSO algorithm called Neighborhood search assisted Particle Swarm Optimization (NPSO) algorithm for data clustering problems has been proposed in this paper. We have proposed two neighborhood search schemes and a centroid updating scheme to improve the performance of the PSO algorithm. NPSO algorithm has been applied to solve the data clustering problems by considering three performance metrics, such as TRace Within criteria (TRW), Variance Ratio Criteria (VRC) and Marriott Criteria (MC). The results obtained by the proposed algorithm have been compared with the published results of basic PSO algorithm, Combinatorial Particle Swarm Optimization (CPSO) algorithm, Genetic Algorithm (GA) and Differential Evolution (DE) algorithm. The performance analysis demonstrates the effectiveness of the proposed algorithm in solving the partitional data clustering problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Xu, R., Wunsch, D.: Survey of Clustering Algorithms. IEEE Transactions on Neural Network 16(3), 645–678 (2005)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE Press, Piscataway (1995)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Survey 31(3), 264–323 (1999)
Paterlini, S., Krink, T.: Differential evolution and particle swarm optimization in partitional clustering. Computational Statistics & Data Analysis 50(5), 1220–1247 (2006)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceeding of the 1998 IEEE World Congress on Computational Intelligence, pp. 69–73. IEEE Press, Piscataway (1998)
Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Proceeding of the 1999 IEEE World Congress on Evolutionary Computing, pp. 1945–1950. IEEE Press, Piscataway (1999)
Eberhart, R., Shi, Y.: Tracking and optimizing dynamic systems with particle swarms. In: Proceeding of 2001 IEEE World Congress on Evolutionary Computing, pp. 94–100. IEEE Press, Piscataway (2001)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simple, maybe better. IEEE Transaction on Evolutionary Computing 8(3), 204–210 (2004)
Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self organization hierarchical particle swarm optimizer with time varying acceleration coefficients. IEEE Transaction on Evolutionary Computing 8(3), 240–255 (2004)
Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variants. IEEE Transaction on System, Man and Cybernetics (Part B) 35(6), 1272–1282 (2005)
Chatterjee, A., Siarry, P.: Non Linear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers and Operations Research 33, 859–871 (2006)
Van Der Merwe, D.W., Engelbrecht, A.P.: Data clustering using particle swarm optimization. In: Proceedings of IEEE Congress on Evolutionary Computing 2003, Canberra, Australia, pp. 215–220 (2003)
Xiao, X., Dow, E.R., Eberhart, R.C., Miled, Z.B., Oppelt, R.J.: Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization. In: Proc of the 17th International Symposium on Parallel and Distributed Processing (PDPS 2003). IEEE Computer Society, Washington, DC (2003)
Chen, C.Y., Ye, F.: Particle swarm optimization algorithm and its applications to clustering analysis. In: Proceedings of IEEE International Conference on Networking, Sensing and Control, pp. 789–794 (2004)
Orman, M.G.H., Salman, A., Engelbrecht, A.P.: Dynamic clustering using Particle Swarm Optimization with application in image segmentation. Pattern Analysis and Application 8(4), 332–344 (2005)
Cohen, S.C.M., De Castro, L.N.: Data Clustering with Particle swarms. In: IEEE Congress on Evolutionary Computations, Vancouver, Canada (2006)
Jarboui, B., Cheikh, M., Siarry, P., Rebai, A.: Combinatorial particle swarm optimization(CPSO) for partitional clustering problem. Applied Mathematics and Computation 192, 337–345 (2007)
Karthi, R., Arumugam, S., RameshKumar, K.: Discrete Particle Swarm Optimization algorithm for Data Clustering. Studies in Computational Intelligence, SCI, vol. 236, pp. 75–88 (2009)
Karthi, R., Arumugam, S., Rameshkumar, K.: Comparative evaluation of Particle Swarm Optimization Algorithms for Data Clustering using real world data sets. International Journal of Computer Science and Network Security 8(1), 203–212 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Karthi, R., Rajendran, C., Rameshkumar, K. (2011). Neighborhood Search Assisted Particle Swarm Optimization (NPSO) Algorithm for Partitional Data Clustering Problems. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22720-2_58
Download citation
DOI: https://doi.org/10.1007/978-3-642-22720-2_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22719-6
Online ISBN: 978-3-642-22720-2
eBook Packages: Computer ScienceComputer Science (R0)