Discriminative semi-supervised clustering analysis with pairwise constreints

Ruan Jian Xue Bao/Journal of Software

Volumn 19, Issue 11, 2008, Pages 2791-2802

  Yin, Xue Song ^a,b   Hu, En Liang ^a   Chen, Song Can ^a  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^b Zhejiang Radio and TV University   (China)

Author keywords

Closure centroid; Clustering analysis; Pairwise constraints; Projection matrix; Semi supervised cludtering

Indexed keywords

ALGORITHMS; CLUSTER ANALYSIS; FLOW OF SOLIDS;

CLOSURE CENTROID; CLUSTERING ANALYSIS; PAIRWISE CONSTRAINTS; PROJECTION MATRIX; SEMI-SUPERVISED CLUDTERING;

CLUSTERING ALGORITHMS;

EID: 56649117423     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. Bar-Hillel A, Hwetz T, Shental N, Weinshall D. Learning a mahalanbois metric from equivalence constraints. Journal of Machine Learning Research, 2005, 6(5): 937-965
2. Wagstaff K. Cardie C, Rogers S, Schroedl S. Constrained K-means clustering with background knowledge. In: Brodley CE, Danyluk AP, eds. Proc. of the 18th Int's Conf. on Machine Learning. Williamstown: Morgan Kaufmann Publishers, 2001. 577-584
3. Bar-Hillel A, Hwetz T, Shental N, Weinshall D. Learning distance function using equivalence relations. In: Fawcett T, Mishra N, eds. Proc. of the 20th Int'l Conf. on Machine Learing. Washington: Morgan Kaufmann Publishers, 2003. 11-18
4. Basu S, Banerjee A, Mooney R J. Semi-Supervised clustering by seeding. In: Sammut C, Hoffmann AG.eds. Proc. of the 19th Int'l Conf. on Machine Learning. Sydney: Morgan Kaufmann Publishers, 2002, 19-26
5. Xing E P, Ng A Y, Jordan M I, Russell S. Distance metric learning with application to clustering with side- information. In: Becher S, Thrun S,Obermayer K.eds. Proc. of the 16th Annual Conf. on Neural Information Processing System. Cambridge: MIT Press, 2003, 505-512
6. Basu S, Banerjee A, Mooney R J. A probabilistic framework for semi-supervised clustering. In: Boulicaut J F, Esposito F, Giannotti F, Pedreschi D.eds. Proc. of the 10th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York: ACM Press, 2004 .59-68
7. Bilenko M, Basu S, Mooney R J. Integrating constraints and metric learning in semi-supervised clustering. In: Brodley C E, ed. Proc. of the 21st Int'l Conf. on Machine Learning. New York: ACM Press, 2004, 81-88
8. Tang W, Xiong H, Zhong S, Wu J. Enhancing semi-supervised clustering: a feature projection perspective. In: Berkhin P,Caruana R, Wu X D.eds. Proc. Of. the 13th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York: ACM Press, 2007, 707-716
9. Basu S, Banerjee A, Mooney R J. Active semi-supervision for pairwise constrained clustering. In: Jonker W, Petkovic M. eds. Proc. of the SIAM Int'l Conf. on Data Mining. Cambridge: MIT Press, 2004, 333-344
10. Yan B, Domeniconi C. An adaptive kernel method for semi-supervised clustering. In: Fürnkranze J, Scheffer T, Spiliopoulou M, eds. Proc. of the 17th European Conf. on Machine Learning. Berlin: Sigma Press, 2006, 18-22
11. Yeung D Y, Chang H. Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints. Pattern Recognition, 2006, 39(5):1007-1010.
12. Beyer K, Goldstein J, Ramakrishnan R, Shaft U. When is " Nearest Neighbors Meaningful "? In: Beeri C, Buneman P, eds. Proc. of the Int'l Conf. on Database Theory. New York: ACM Press, 1999. 217-235.
13. Ding C H, Li T. Adaptive dimension reduction using discriminant analysis and K-means clustering. In: Ghahramani Z, ed. Proc. of the 19th Int'l Conf. on Machine Learning. New York: ACM Press, 2007. 521-528.
14. Zhang D Q, Zhou Z H, Chen S C. Semi-Supervised dimensionality reduction. In: Mandoiu I, Zelikovsky A, eds. Proc. of the 7th SIAM Int'l Conf. on Data Mining. Cambridge: MIT Press, 2007. 629-634.
15. Ye J P, Zhao Z, Liu H. Adaptive distance metric learning for clustering. In: Bishop CM, Frey B, eds. Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. Madison: IEEE Computer Society Press, 2007. 1-7.
16. Chen J H, Zhao Z, Ye J P, Liu H. Nonlinear adaptive distance metric learning for clustering. In: Berkhin P, Caruana R, Wu XD, eds. Proc. of the 13th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York: ACM Press, 2007. 123-132.
17. Saul L K, Roweis S T. Think globally, fit locally: Unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research, 2003, 4(3):119-155.
18. Schultz M, Joachims T. Learning a distance metric from relative comparisons. In: Thrun S, Saul LK, Sch-lkopf B, eds. Proc. of the 17th Annual Conf. on Neural Information Processing System. Cambridge: MIT Press, 2004. 41-48.
19. Torre F, Kanade T. Discriminative cluster analysis. In: William WC, Andrew M, eds. Proc. of the 19th Int'l Conf. on Machine Learning. New York: ACM Press, 2006. 241-248.