Size regularized cut for data clustering

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages 211-218

  Chen, Yixin ^a   Zhang, Ya ^b   Ji, Xiang ^c  

^a UNIVERSITY OF NEW ORLEANS   (United States)

^b University of Kansas   (United States)

^c NEC LABORATORIES AMERICA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING PROCESS; DATA CLUSTERING; DATA SETS; EIGENVALUE PROBLEM; NORMALIZED CUTS; NP COMPLETE; OPTIMIZATION PROBLEMS; PRIOR KNOWLEDGE; RELATIVE SIZES; SPECTRAL CLUSTERING METHODS;

APPROXIMATION ALGORITHMS; CLUSTERING ALGORITHMS; EIGENVALUES AND EIGENFUNCTIONS;

CLUSTER ANALYSIS;

EID: 64049099517     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. S. Arora, D. Karger, and M. Karpinski, "Polynomial Time Approximation Schemes for Dense Instances of NP-hard Problems," Proc. ACM Symp. on Theory of Computing, pp. 284-293, 1995.
2. A. Banerjee and J. Ghosh, "On Scaling up Balanced Clustering Algorithms," Proc. SIAM Int'l Conf. on Data Mining, pp. 333-349, 2002.
3. P. K. Chan, D. F. Schlag, and J. Y. Zien, "Spectral k-Way Ratio-Cut Partitioning and Clustering," IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, 13:1088-1096, 1994.
4. M. Charikar, S. Khuller, D. M. Mount, and G. Narasimhan, "Algorithms for Facility Location Problems with Outliers," Proc. ACM-SIAM Symp. on Discrete Algorithms, pp. 642-651, 2001.
5. I. S. Dhillon, "Co-clustering Documents andWords using Bipartite Spectral Graph Partitioning," Proc. ACM SIGKDD Conf. Knowledge Discovery and Data Mining, pp. 269-274, 2001.
6. C. Ding, "Data Clustering: Principal Components, Hopfield and Self-Aggregation Networks," Proc. Int'l Joint Conf. on Artificial Intelligence, pp. 479-484, 2003.
7. C. Ding, X. He, H. Zha, M. Gu, and H. Simon, "Spectral Min-Max Cut for Graph Partitioning and Data Clustering," Proc. IEEE Int'l Conf. Data Mining, pp. 107-114, 2001.
8. G. H. Golub and C. F. Van Loan, Matrix Computations, John Hopkins Press, 1999.
9. R. Kannan, S. Vempala, and A. Vetta, "On Clusterings - Good, Bad and Spectral," Proc. IEEE Symp. on Foundations of Computer Science, pp. 367-377, 2000.
10. D. R. Karget and M. Minkoff, "Building Steiner Trees with Incomplete Global Knowledge," Proc. IEEE Symp. on Foundations of Computer Science, pp. 613-623, 2000
11. B. Kernighan and S. Lin, "An Efficient Heuristic Procedure for Partitioning Graphs," The Bell System Technical Journal, 49:291-307, 1970.
12. A. Y. Ng, M. I. Jordan, and Y. Weiss, "On Spectral Clustering: Analysis and an Algorithm," Advances in Neural Information Processing Systems 14, pp. 849-856, 2001.
13. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C, second edition, Cambridge University Press, 1992.
14. A. Rahimi and B. Recht, "Clustering with Normalized Cuts is Clustering with a Hyperplane," Statistical Learning in Computer Vision, 2004.
15. J. Shi and J. Malik, "Normalized Cuts and Image Segmentation," IEEE Trans. on Pattern Analysis and Machine Intelligence, 22:888-905, 2000.
16. K. Wagstaff, C. Cardie, S. Rogers, and S. Schrodl, "Constrained K-means Clustering with Background Knowledge," Proc. Int'l Conf. on Machine Learning, pp. 577-584, 2001.
17. E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell, "Distance Metric Learning, with Applications to Clustering with Side Information," Advances in Neural Information Processing Systems 15, pp. 505-512, 2003.
18. X. Yu and J. Shi, "Segmentation Given Partial Grouping Constraints," IEEE Trans. on Pattern Analysis and Machine Intelligence, 26:173-183, 2004.
19. H. Zha, X. He, C. Ding, H. Simon, and M. Gu, "Spectral Relaxation for K-means Clustering," Advances in Neural Information Processing Systems 14, pp. 1057-1064, 2001.