Farthest centroids divisive clustering

Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

Volumn , Issue , 2008, Pages 232-238

  Fang, Haw Ren ^a   Saad, Yousef ^b  

^a ARGONNE NATIONAL LABORATORY   (United States)

^b University of Minnesota   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE ALGORITHMS; CLUSTERING METHODS; CLUSTERING QUALITIES; COMPUTATIONAL COSTS; DATA ENTRIES; DIVISIVE CLUSTERING; EUCLIDEAN SPACES; EXPERIMENTAL EVIDENCES; INTEGER PROGRAMMING PROBLEMS; K-MEANS ALGORITHMS; K-MEANS TECHNIQUES; NP-HARD; SPECTRAL CLUSTERING METHODS;

INTEGER PROGRAMMING; RECURSIVE FUNCTIONS; ROBOT LEARNING;

CLUSTERING ALGORITHMS;

EID: 60649118355     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2008.141     Document Type: Conference Paper

References (16)

1. D. L. Boley. Principal direction divisive partitioning. Data Mining and Knowledge Discovery, 2 (4):324-344, 1998.
2. L. Bottou and Y. Bengio. Convergence properties of the K means algorithms. In G. Tesauro, D. Touretzky, and T. Leen, editors. Advances in Neural Information Processing Systems, volume 7, pages 585-592. The MIT Press, 1995.
3. T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction to Algorithms. The MIT Press, 2nd edition, 2001.
4. H. Fang and Y. Saad. Farthest centroids divisive clustering. University of Minnesota Supercomputing Institute Research Report UMSI-2008-5, 2008.
5. F. Juhász and K. Mályusz. Problems of cluster analysis from the viewpoint of numerical analysis. In Colloquia Mathematica Societatis Janos Bolyai, volume 22, pages 405-415, North-Holland, Amsterdam, 1980.
6. G. Karypis and V. Kumar. Analysis of multilevel graph partitioning. In Supercomputing'95: Proceedings of the 1995 ACM/IEEE Conference on Supercomputing (CDROM). ACM Press, 1995. Article No. 29.
7. B. W. Kernighan and S. Lin. An efficient heuristic procedure for partitioning graphs. In Bell System Technical I., volume 29, pages 291-307, 1970.
8. S.Lin. Computer solutions of the traveling salesman problem. In Bell System Tech. J., volume 44, pages 2245-2269, 1965.
9. A. Paccanaro, J. A. Casbon, and M. A. S. Saqi. Spectral clustering of protein sequences. Nucleic Acids Research, 34 (5): 1571-1580, 2006.
10. A. Pothen, H. D. Simon, and K.-P. Liou. Partitioning sparse matrices with eigenvectors of graphs. SIAM J. Matrix Anal. and Appl., 11 (3):430-452, 1990.
11. C. Rosenberger and K. Chehdi. Unsupervised clustering method with optimal estimation of the number of clusters: Application to image segmentation. In 15th International Conference on Pattern Recognition, volume 1, page 1656, 2000.
12. F. S. Samaria and A. C. Harter. Parameterisation of a stochastic model for human face identification. In Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, pages 138-142, 1994.
13. B. L. Saux and N. Boujemaa. Unsupervised robust clustering for image database categorization. In IEEE-IAPR International Conference on Pattern Recognition, August 2002.
14. S. Savaresi, D. L. Boley, S. Bittanti, and G. Gazzaniga. Choosing the cluster to split in bisecting divisive clustering algorithms. In Second SIAM International Conference on Data Mining, pages 299-314, 2002.
15. A. Schrijver. Theory of Linear and Integer Programming. John Wiley and Sons, 1998.
16. D. Tritchler, S. Fallah, and J. Beyene. A spectral clustering method for microarray data. Comput. Stat. & Data Anal., 49:63-76, 2005.