Abstract
There exist several approaches for solving clustering problems. Hopfield networks and self-organizing maps are the main neural approaches studied for solving clustering problems. Criticism of these approaches includes the tendency of the Hopfield network to produce infeasible solutions and the lack of generalization of the self-organizing approaches. Genetic algorithms are the other most studied bio-inspired approaches for solving optimization problems as the clustering problems. However, the requirement of tuning many internal parameters and operators is the main disadvantage of the genetic algorithms. This paper proposes a new technique which enables feasible solutions, removes the tuning phase, and improves solutions quality of clustering problems. Moreover, several biology inspired approaches are analyzed for solving traditional benchmarks.
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.
References
J. MacQueen: Some methods for classification and analysis of multivariate observations. In 5th Berkeley Symp. Mathematical Statistics and Probability, pp. 281–297 (1967)
J. H. Holland: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975).
D. E. Goldberg: Genetic algorithms in search, optimization and machine learning. Addison Wesley (1989).
J. Hopfield & D. Tank: Neural computation of decisions in optimization problems. Biological Cybernetics 52, 141–152 (1985).
E. Dominguez & J. Muñoz: A recurrent neural network for location problems. In 50th Regional Science Association International, Philadelphia (2003).
E. Dominguez & J. Muñoz: Hierarchical neural techniques for location problems. In IASTED Artificial Intelligence and Soft-Computing, Marbella, Spain (2004).
O. Alp, E. Erkut & Z. Drezner: An efficient genetic algorithm for the p-median problem. Annals of Operations Research 122, 21–42 (2003).
Resende & Werneck: A hybrid heuristic for the p-median problem. Journal of Heuristics 10(1), 59–88 (2004).
E. Domínguez & J. Muñoz: An efficient neural network for the p-median problem. LNAI vol. 2527, pp. 460–469. Springer (2002).
E. Dominguez & J. Muñoz: Bidirectional neural network for clustering problems, LNAI vol. 3315, pp. 788–798. Springer (2004).
J. E. Beasley: OR-Library. Distributing test problems by electronic mail. Journal of the Operational Research Society 41(11), 1069–1072 (1990.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Domínguez, E., Muñoz, J. (2007). A Hybrid Algorithm for Solving Clustering Problems. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-74972-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74971-4
Online ISBN: 978-3-540-74972-1
eBook Packages: EngineeringEngineering (R0)