Abstract
The goal of clustering is to cluster the objects into groups that are internally homogeneous and heterogeneous from group to group. Clustering is an important tool for diversely intelligent systems. So, many works have been researched in the machine learning algorithms. But, some problems are still shown in the clustering. One of them is to determine the optimal number of clusters. In K-means algorithm, the number of cluster K is determined by the art of researchers. Another problem is an over fitting of learning models. The majority of learning algorithms for clustering are not free from the problem. Therefore, we propose a competitive co-evolving support vector clustering. Using competitive co-evolutionary computing, we overcome the over fitting problem of support vector clustering which is a good learning model for clustering. The number of clusters is efficiently determined by our competitive co-evolving support vector clustering. To verify the improved performances of our research, we compare competitive co-evolving support vector clustering with established clustering methods using the data sets form UCI machine learning repository.
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
Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support Vector Clustering. Journal of Machine Learning Research 2, 125–137 (2001)
Bezdek, J.C., Boggavarapu, S., Hall, L.O., Bensaid, A.: Genetic algorithm guided clustering. IEEE World Congress on Computational Intelligence 1, 34–39 (1994)
Bock, H.H.: On Some Significance Tests in Cluster Analysis. Journal of Classification 2, 77–108 (1985)
Burges, C.J.: A Tutorial on Support Vector Machine for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Cherkassky, V., Mulier, F.: Learning from Data - Concepts, Theory, and Methods. John Wiley & Sons, Inc., Chichester (1998)
Chiang, J.C., Wang, J.S.: A Validity-Guided Support Vector Clustering Algorithm for Identification of Optimal Cluster Configuration. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 3613–3618 (2004)
Davison, A.C.: Bootstrap methods and their application. Cambridge University Press, Cambridge (1997)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Everitt, B.S.: Unresolved Problems in Cluster Analysis. Biometrics 35, 169–181 (1979)
Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis, Arnold (2001)
Fletcher, R.: Practical Methods of Optimization. John Wiley & Sons, Chichester (1989)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Hartigan, J.A.: Statistical Theory in Clustering. Journal of Classification 2, 63–76 (1985)
Haykin, S.: Neural Networks. Prentice Hall, Englewood Cliffs (1999)
Jun, S.H.: Web Usage Mining Using Support Vector Machine. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 349–356. Springer, Heidelberg (2005)
Lee, J., Lee, D.: An Improved Cluster Labeling Method for Support Vector Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 461–464 (2005)
Ling, P., Wang, Y., Lu, N., Wang, J.Y., Liang, S., Zhou, C.G.: Two-Phase Support Vector Clustering for Multi-Relational Data Mining. In: Proceedings of the International Conference on Cyberworlds (2005)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Mitchell, T.M.: An introduction to Genetic Algorithms. MIT Press, Cambridge (1998)
Ribeiro, B.: On Data Based Learning using Support Vector Clustering. In: Proceedings of the 9th International Conference on Neural Information Processing, vol. 5, pp. 2516–2521 (2002)
Sun, B.Y., Huang, D.S.: Support Vector Clustering for Multiclass Classification Problems. IEEE Evolutionary Computation Congress 2, 1480–1485 (2003)
Tax, D.M.J., Duin, R.P.W.: Support Vector Domain Description. Pattern Recognition Letters 20, 1191–1199 (1999)
UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/MLRepository.html
Vapnik, V.Z.: Statistical Learning Theory. John Wiley & Sons, Inc., Chichester (1998)
Vapnik, V.: An Overview of Statistical Learning Theory. IEEE Transactions Networks 10, 903–988 (2002)
Wang, J., Wu, X., Zhang, C.: Support vector machine based on K-means clustering for real-time business intelligence systems. International Journal of Business Intelligence and Data Mining 1(1), 54–64 (2005)
Yang, J., Estivill-Castro, V., Chalup, S.K.: Support Vector Clustering Through Proximity Graph Modeling. In: Proceedings of Ninth International Conference of Neural Information Processing, pp. 898–903 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jun, SH., Oh, KW. (2006). A Competitive Co-evolving Support Vector Clustering. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_96
Download citation
DOI: https://doi.org/10.1007/11893028_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
Online ISBN: 978-3-540-46480-8
eBook Packages: Computer ScienceComputer Science (R0)