Abstract
In this paper, we present a linked-list based encoding scheme for multiple objectives based genetic algorithm (GA) to identify clusters in a partition. Our approach obtains the optimal partitions for all the possible numbers of clusters in the Pareto Optimal set returned by a single genetic GA run. The performance of the proposed approach has been tested using two well-known data sets, namely Iris and Ruspini. The obtained results are promising and demonstrate the applicability and effectiveness of the proposed approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pen, J.M., Lozano, J., Larran, J.: An empirical comparison of four initialization methods for the k-means algorithm. Pattern Recognition Letters 20(10), 1027–1040 (1999)
Berkhin, P.: Survey of clustering data mining techniques. Technical report, Accrue Software, San Jose, CA (2002)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (2000)
Bremner, A.: Reviews: The Book of Numbers, by John Horton Conway and Richard K. Guy. American Mathematical Monthly 104(9), 884–899 (1997)
Burke, D.S., De Jong, K.A., Grefenstette, J.J., Ramsey, C.L., Wu, A.S.: Putting more genetics into genetic algorithms. Evolutionary Computation 6(4), 387–410 (Winter 1998)
Cole, R.M.: Clustering with genetic algorithms. Master’s thesis, Nedlands 6907, Australia (1998)
Dhillon, I., Fan, J., Guan, Y.: Efficient clustering of very large document collections. In: Grossman, R., Kamath, C., Naburu, R. (eds.) Data Mining for Scientific and Engineering Applications. Kluwer Academic Publishers, Dordrecht (2001)
Falkenauer, E.: Genetic Algorithms and Grouping Problems. John Wiley&Sons, Chichester (1998)
Falkenauer, E.: A new representation and operators for genetic algorithms applied to grouping problems. Evolutionary Computation 2(2), 123–144 (1994)
Gibson, D., Kleinberg, J.M., Raghavan, P.: Clustering categorical data: An approach based on dynamical systems. VLDB Journal: Very Large Data Bases 8(3–4), 222–236 (2000)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Guha, S., Rastogi, R., Shim, K.: CURE: an efficient clustering algorithm for large databases. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, Seattle, Washington, USA, vol. 27(2), pp. 73–84 (1998)
Han, J., Kamer, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Horn, J., Nafpliotis, N., Goldberg, D.E.: A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, vol. 1, pp. 82–87 (1994)
Jones, D.A., Beltramo, M.A.: Solving partitioning problems with genetic algorithms. In: Belew, L.B., Booker, R.K. (eds.) Proceedings of the 4th International Conference on Genetic Algorithms, pp. 442–449. Morgan Kaufmann, San Francisco (1991)
Krishna, K., Murty, M.: Genetic k-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics - PartB: Cybernetics 29(3), 433–439 (1999)
Maulik, U., Bandyopadhyay, S.: Genetic algorithm-based clustering technique. Pattern Recognition 33, 1455–1465 (2000)
Ng, R., Han, J.: Efficient and effective clustering method for spatial data mining. In: Proc. of 1994 Int’l. Conf. on Very Large Data Bases (VLDB 1994), September 1994, pp. 144–155 (1994)
Radcliffe, N.J.: Forma analysis and random respectful recombination. In: Belew, L.B., Booker, R.K. (eds.) Proceedings of the 4th International Conference on Genetic Algorithms, pp. 222–229. Morgan Kaufmann, San Francisco (1991)
Richardson, J.T., Palmer, M.R., Liepina, G., Hilliard, M.: Some guidlines for genetic algorithms with penalty functions. In: Proc. of the 3rd Int. Conf. on Genetic Algorithms, pp. 191–192. Morgan Kaufman, San Francisco (1989)
Ruspini, E.H.: Numerical methods for fuzzy clustering. Inform. Sci. 2(3), 19–150 (1970)
Sarafis, I., Zalzala, A.M.S., Trinder, P.: A genetic rule-based data clustering toolkit. In: Proceedings of the 2002 Congress on Evolutionary Computation CEC 2002, pp. 1238–1243 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Du, J., Korkmaz, E.E., Alhajj, R., Barker, K. (2005). Alternative Clustering by Utilizing Multi-objective Genetic Algorithm with Linked-List Based Chromosome Encoding. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_34
Download citation
DOI: https://doi.org/10.1007/11510888_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
Online ISBN: 978-3-540-31891-0
eBook Packages: Computer ScienceComputer Science (R0)