The global kernel κ-means algorithm for clustering in feature space

IEEE Transactions on Neural Networks

Volumn 20, Issue 7, 2009, Pages 1181-1194

  Tzortzis, Grigorios F ^a   Likas, Aristidis C ^a  

^a UNIVERSITY OF IOANNINA   (Greece)

Author keywords

means; Clustering; Graph partitioning; Kernel means

Indexed keywords

CLUSTER INITIALIZATION; CLUSTERING; COMPUTATIONAL COSTS; DATA SETS; FEATURE SPACE; GLOBAL SEARCH PROCEDURE; GRAPH PARTITIONING; INCREMENTAL APPROACH; KERNEL-BASED CLUSTERING; LOCAL MINIMUMS; MEANS CLUSTERING ALGORITHM; NEAR-OPTIMAL SOLUTIONS; SEARCH PROCEDURES; SOLUTION QUALITY; WEIGHTED DATA;

CLUSTERING ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER PROGRAM; COMPUTER SIMULATION; SIGNAL PROCESSING; STATISTICAL ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOFTWARE; SOFTWARE VALIDATION;

EID: 67949112664     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2009.2019722     Document Type: Article

References (25)

1. R. Xu and D. Wunsch, II, "Survey of clustering algorithms," IEEE Trans. Neural Netw., vol. 16, no. 3, pp. 645-678, May 2005.
2. A. Likas, N. Vlassis, and J. J. Verbeek, "The global k-means clustering algorithm," Pattern Recognit., vol. 36, no. 2, pp. 451-461, Feb. 2003.
3. B. Scholkopf, A. J. Smola, and K.-R. Muller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput., vol. 10, no. 5, pp. 1299-1319, Jul. 1998.
4. D. Lashkari and P. Golland, "Convex clustering with exemplar-based models," Adv. Neural Inf. Process. Syst., vol. 20, pp. 825-832, 2008.
5. I. S. Dhillon, Y. Guan, and B. Kulis, "Kernel k-means, spectral clustering and normalized cuts," in Proc. 10th ACM SIGKDD Int. Conf. Knowl. Disc. Data Mining, 2004, pp. 551-556.
6. I. S. Dhillon, Y. Guan, and B.Kulis, "A unified viewof kernel k means, spectral clustering and graph cuts," Univ. Texas, Austin, TX, Tech. Rep. TR-04-25, 2004.
7. I. S. Dhillon, Y. Guan, and B. Kulis, "Weighted graph cuts without eigenvectors:A multilevel approach," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 11, pp. 1944-1957, Nov. 2007.
8. M. Filippone, F. Camastra, F. Masulli, and S. Rovetta, "A survey of kernel and spectral methods for clustering," Pattern Recognit., vol. 41, no. 1, pp. 176-190, Jan. 2008.
9. G. Tzortzis and A. Likas, "The global kernel k-means clustering algorithm," in Proc. IEEE Int. Joint Conf. Neural Netw., Jun. 2008, pp. 1977-1984.
10. A. M. Bagirov, "Modified global k-means algorithm for sum-ofsquares clustering problems," Pattern Recognit., vol. 41, no. 10, pp. 3192-3199, Oct. 2008.
11. J. Li, D. Tao, W. Hu, and X. Li, "Kernel principle component analysis in pixels clustering," in Proc. IEEE/WIC/ACM Int. Conf. Web Intell. Sep. 2005, pp. 786-789.
12. R. Zhang and A. I. Rudnicky, "A large scale clustering scheme for kernel k-means," in 'Proc. 16th Int. Conf. Pattern Recognit. 2002, pp. 289-292.
13. B. J. Frey and D. Dueck, "Clustering by passing messages between data points," Science, vol. 315, pp. 972-976, 2007.
14. A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh, "Clustering with Bregman divergences," J. Mach. Learn. Res., vol. 6, pp. 1705-1749. 2005.
15. I. Csiszár and P. C. Shields, "Information theory and statistics: A tutorial," Commun. Inf. Theory, vol. 1, no. 4, pp. 417-528, 2004.
16. A. Y. Ng, M. I. Jordan, and Y.Weiss, "On spectral clustering: Analysis and an algorithm," Adv. Neural Inf. Process. Syst., vol. 14, pp. 849-856, 2002.
17. H. Zha, X. He, C. H. Q. Ding, M. Gu, and H. D. Simon, "Spectral relaxation for k-means clustering," Adv. Neural Inf. Process. Syst., vol. 14, pp. 1057-1064, 2002.
18. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, Aug. 2000.
19. S. X. Yu and J. Shi, "Multiclass spectral clustering," inProc. 9th IEEE Int. Conf. Comput. Vis., 2003, pp. 313-319.
20. M. Gong, L. Jiao, L. Bo, L. Wang, and X. Zhang, "Image texture classification using a manifold distance based evolutionary clustering method," Opt. Eng., vol. 47, no. 7, p. 077201, Jul. 2008.
21. A. Asuncion and D. Newman, UCI Machine Learning Repository, Univ. California at Irvine, Irvine, CA, 2007 [Online]. Available: http:// www.ics.uci.edu/~mlearn/MLRepository.html
22. Olivetti and Oracle Research Laboratory, The Olivetti & Oracle Research Laboratory Database of Faces, Cambridge, U.K., 1994 [Online]. Available: http://mambo.ucse.edu/psl/olivetti.html
23. D. Dueck and B. Frey, "Non-metric affinity propagation for unsupervised image categorization," in Proc. 11th IEEE Int. Conf. Comput. Vis. Oct. 2007, pp. 1-8.
24. C.Walshaw, The Graph Partitioning Archive, 2007 [Online].Available: http://staffweb.cms.gre.ac.uk/~c.walshaw/partition/
25. T. M. Cover and J. A. Thomas, Elements of Information Theory. New York: Wiley, 1991.