Kernel K-means for categorical data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3646 LNCS, Issue , 2005, Pages 46-56

  Couto, Julia ^a  

^a JAMES MADISON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER SCIENCE; DATA ACQUISITION; DATABASE SYSTEMS;

CATEGORICAL DATA; HAMMING DISTANCE; KERNEL FUNCTION; KERNEL K-MEANS;

DATA REDUCTION;

EID: 33745203989     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11552253_5     Document Type: Conference Paper

References (21)

1. Andritsos, P., Tsaparas, P., Miller, R. J., Sevcik., K. C.: LIMBO: Scalable Clustering of Categorical Data. In Proceedings of the 9th International Conference on Extending Database Technology (EDBT 2004), Heraklion, Crete, Greece, March 2004.
2. Barbara, D., Couto, J., Li Y.: Coolcat: An Entropy-based algorithm for Categorical Clustering. In Proceedings of the 11th ACM Conference on Information and Knowledge Management (CIKM 02), McLean, Virginia, USA, November 2002, ACM Press, pp. 582-589.
3. Ben-hur, A., Horn, D., Siegelmann, H.T., Vapnik V.: Support Vector Clustering. Journal of Machine Learning Research 2, pp. 125-137.
4. Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. http://www.ics.uci.edu/~mlearn/MLRepository.html. University of California, Department of Information and Computer Science, Irvine, CA.
5. V. Ganti, J. Gehrke, and R. Ramakrishnan.: CACTUS: Clustering Categorical Data using Summaries. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, USA, August 1999, ACM Press, pp. 73-83.
6. Girolami, M.: Mercer Kernel Based Clustering in Feature Space. IEEE Transactions on Neural Networks, 13(4), pp. 780-784, 2002.
7. Gibson, D., Kleinberg, J., Raghavan, P.: Clustering Categorical Data: An Approach Based on Dynamical Systems. In Proceedings of the 24th International Conference on Very Large Data Bases, (VLDB), New York, NY, USA, August 1998, Morgan Kaufmann, pp. 311-322.
8. Gluck, A., Corter, J.: Information, Uncertainty, and the Utility of Categories. In Proceedings of the 7th Annual Conference of the Cognitive Science Society, Irvine, California, 1985, Laurence Erlbaum Associates, pp. 283-287.
9. Guha, S., Rastogi, R, Shim, K.: ROCK: A Robust Clustering Algorithm for Categorical Attributes. Journal of Information Systems, 25(5), pp. 345-366, 2000.
10. M. Halkidi, Y. Batistakis, M. Vazirgiannis.: On Clustering Validation Techniques. Journal of Intelligent Information Systems, 17(2-3), pp. 107-145, 2001.
11. Huang, Z.: Extensions to the K-Means Algorithm for Clustering Large Data Sets with Categorical Values, Data Mining and Knowledge Discovery, 2(3), pp. 283-304, 1998.
12. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, NJ, 1988.
13. Katsavounidis, I., Kuo, C., Zhang,Z.: A New Initialization Technique for Generalized Lloyd Iteration. IEEE Signal Processing Letters, 1(10), pp. 144-146, 1994.
14. Kondor, R.I.,Lafferty, J.: Diffusion Kernels on Graphs and Other Discrete Structures, In Sammut,C. and Hoffmann, A.G. (eds), Machine Learning. In Proceedings of the 19th International Conference on Machine Learning (ICML 2002), Morgan Kaufmann, pp. 315-322.
15. Larsen, B., Aone, C.: Fast and Effective Text Mining Using Linear-time Document Clustering. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, August 1999, ACM Press, pp. 16-22.
16. H. Lodhi, J. Shawe-Taylor, N. Cristiani and C. Watkins.: Text Classification using String Kernels. Journal of Machine Learning Research, 2, pp. 419-444.
17. Shawe-Taylor, J., Cristiani, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, 2003.
18. Slonim, N., Tibshy, N.: Agglomerative Information Bottleneck. In Proceedings of the Neural Information Processing Systems Conference 1999 (NIPS99), Beckenridge, 1999.
19. Steinbach, M., Karypis, G., Kumar, V.: A Comparison of Document Clustering Technique, Technical Report #00-034, University of Minnesota, Department of Computer Science and Egineering.
20. Zaki, M. J., Peters, M.: CLICK: Mining Subspaces Clusters in Categorical Data via K-partite Maximal Cliques. TR 04-11, CS Dept., RPI, 2004.
21. Zhang, R., Rudnicky, A.: A Large Scale Clustering Scheme for Kernel K-means. In Proceedings of the 16th International Conference on Pattern Recognition (ICPR 02), Quebec City, Canada, August 2002, pp. 289-292.