Semi-supervised graph clustering: A kernel approach

Machine Learning

Volumn 74, Issue 1, 2009, Pages 1-22

  Kulis, Brian ^a   Basu, Sugato ^b   Dhillon, Inderjit ^a   Mooney, Raymond ^a  

^a University of Texas at Austin   (United States)

^b GOOGLE INC   (United States)

Author keywords

Graph clustering; Kernel k means; Semi supervised clustering; Spectral learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; HIDDEN MARKOV MODELS; IMAGE SEGMENTATION; INFORMATION MANAGEMENT; KNOWLEDGE BASED SYSTEMS; PROBABILISTIC LOGICS; SUPPORT VECTOR MACHINES; VECTORS;

CURRENT STATES; DATA SETS; EUCLIDEAN DISTANCES; GRAPH CLUSTERING; GRAPH DATUMS; HIDDEN MARKOV RANDOM FIELDS; INPUT DATUMS; KERNEL APPROACHES; KERNEL K-MEANS; OBJECTIVE FUNCTIONS; PAIRWISE CONSTRAINTS; PENALTY FUNCTIONS; SEMI-SUPERVISED CLUSTERING; SPECTRAL LEARNING; SUPERVISED ALGORITHMS; VECTOR DATUMS;

CLUSTERING ALGORITHMS;

EID: 58149202361     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-008-5084-4     Document Type: Article

References (43)

1. Bansal, N., Blum, A., & Chawla, S. (2002). Correlation clustering. In Proceedings of the 43rd IEEE symposium on foundations of computer science (FOCS-02) (pp. 238-247).
2. Bar-Hillel, A., Hertz, T., Shental, N., & Weinshall, D. (2003). Learning distance functions using equivalence relations. In Proceedings 20th international conference on machine learning (pp. 11-18).
3. Basu, S., Banerjee, A., & Mooney, R. J. (2002). Semi-supervised clustering by seeding. In Proceedings of 19th international conference on machine learning (ICML-2002) (pp. 19-26).
4. Basu, S., Banerjee, A., & Mooney, R. J. (2004a). Active semi-supervision for pairwise constrained clustering. In Proceedings 4th SIAM international conference on data mining.
5. Basu, S., Bilenko, M., & Mooney, R. J. (2004b). A probabilistic framework for semi-supervised clustering In Proceedings of 10th ACM SIGKDD international conference on knowledge discovery and data mining (KDD-2004) (pp. 59-68).
6. Bie, T. D., Momma, M., & Cristianini, N. (2003). Efficiently learning the metric using side-information. In Lecture notes in artificial intelligence : Vol. 2842. Proceedings of the 14th international conference on algorithmic learning theory (ALT2003) (pp. 175-189). Berlin: Springer.
7. Bilenko, M., & Basu, S. (2004). A comparison of inference techniques for semi-supervised clustering with hidden Markov random fields. In Proceedings of the ICML-2004 workshop on statistical relational learning and its connections to other fields (SRL-2004), Banff, Canada.
8. Bilenko, M., Basu, S., & Mooney, R. (2004). Integrating constraints and metric learning in semi-supervised clustering. In Proceedings of the 21st international conference on machine learning.
9. P. Chan M. Schlag J. Zien 1994 Spectral k-way ratio cut partitioning IEEE Transactions CAD-Integrated Circuits and Systems 13 1088 1096
10. Chang, H., & Yeung, D. (2004). Locally linear metric adaptation for semi-supervised clustering. In Proceedings of the twenty-first international conference on machine learning (ICML) (pp. 153-160).
11. Chapelle, O., Schölkopf, B., & Zien, A. (Eds.) (2006). Semi-supervised learning. Cambridge: MIT Press.
12. Charikar, M., Guruswami, V., & Wirth, A. (2003). Clustering with qualitative information. In Proceedings of the 44th annual IEEE symposium on foundations of computer science (pp. 524-533).
13. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. New York: Wiley-Interscience.
14. Cristianini, N., & Shawe-Taylor, J. (2000). Introduction to support vector machines. Cambridge: Cambridge University Press.
15. Davidson, I., & Ravi, S. S. (2005a). Clustering with constraints: feasibility issues and the k-means algorithm. In Proceedings of the 2005 SIAM international conference on data mining.
16. Davidson, I., & Ravi, S. S. (2005b). Hierarchical clustering with constraints: theory and practice. In Proceedings of the ninth European principles and practice of KDD (PKDD) (pp. 59-70).
17. Demaine, E. D., & Immorlica, N. (2003). Correlation clustering with partial information. In Proceedings of the 6th international workshop on approximation algorithms for combinatorial optimization problems and 7th international workshop on randomization and approximation techniques in computer science (RANDOM-APPROX).
18. Demiriz, A., Bennett, K. P., & Embrechts, M. J. (1999). Semi-supervised clustering using genetic algorithms. In Artificial neural networks in engineering (ANNIE-99) (pp. 809-814).
19. Dhillon, I., Guan, Y., & Kulis, B. (2004a). Kernel k-means, spectral clustering and normalized cuts. In Proceedings of the 10th international conference on knowledge discovery and data mining (pp. 551-556).
20. Dhillon, I., Guan, Y., & Kulis, B. (2004b). A unified view of kernel k-means, spectral clustering and graph cuts (Tech. rep. TR-04-25). University of Texas at Austin.
21. I. Dhillon Y. Guan B. Kulis 2007 Weighted graph cuts without eigenvectors: a multilevel approach IEEE Transactions on Pattern Analysis and Machine Intelligence 29 11 1944 1957
22. Duda, R. O., & Hart, P. E. (1973). Pattern classification and scene analysis. New York: Wiley.
23. Grauman, K., & Darrell, T. (2005). The pyramid match kernel: discriminative classification with sets of image features. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 1458-1465).
24. Kamvar, S. D., Klein, D., & Manning, C. (2003). Spectral learning. In Proceedings of the 17th international joint conference on artificial intelligence (pp. 561-566).
25. Klein, D., Kamvar, D., & Manning, C. (2002). From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering. In Proceedings of the 19th international conference on machine learning (pp. 307-314).
26. Kleinberg, J., & Tardos, E. (1999). Approximation algorithms for classification problems with pairwise relationships: metric labeling and Markov random fields. In: Proceedings of the 40th IEEE symposium on foundations of computer science (FOCS-99), (pp. 14-23).
27. Kulis, B., Basu, S., Dhillon, I., & Mooney, R. (2005). Semi-supervised graph clustering: a kernel approach. In Proceedings of the 22nd international conference on machine learning (pp. 457-464).
28. Kulis, B., Surendran, A., & Platt, J. (2007). Fast low-rank semidefinite programming for embedding and clustering. In Proceedings 11th international conference on AI and statistics (AISTATS).
29. Lange, T., Law, M. H. C., Jain, A. K., & Buhmann, J. M. (2005). Learning with constrained and unlabelled data. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR).
30. Law, M. H. C., Topchy, A., & Jain, A. K. (2005). Model-based clustering with probabilistic constraints. In Proceedings of the 2005 SIAM international conference on data mining (pp. 641-645).
31. I. Lee S. V. Date A. T. Adai E. M. Marcotte 2004 A probabilistic functional network of yeast genes Science 306 5701 1555 1558
32. Lu, Z., & Leen, T. (2005). Semi-supervised learning with penalized probabilistic clustering. In Advances in neural information processing systems.
33. Meila, M., & Shi, J. (2001). A random walks view of spectral segmentation. In Proceedings of the 8th international workshop on artificial intelligence and statistics (AISTATS).
34. H. Ogata S. Goto K. Sato W. Fujibuchi H. Bono M. Kanehisa 1999 KEGG: Kyoto encyclopedia of genes and genomes Nucleic Acids Research 27 29 34
35. J. Shi J. Malik 2000 Normalized cuts and image segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence 22 8 888 905
36. J. Sinkkonen S. Kaski 2002 Clustering based on conditional distributions in an auxiliary space Neural Computation 14 1 217 239
37. Smola, A. J., & Kondor, R. (2003). Kernels and regularization on computational graphs. In Proceedings conference on computational learning theory (COLT) (pp. 144-158).
38. Strehl, A., Ghosh, J., & Mooney, R. (2000). Impact of similarity measures on web-page clustering. In Workshop on artificial intelligence for web search (AAAI).
39. Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained k-means clustering with background knowledge. In Proceedings of the 18th international conference on machine learning (pp. 577-584).
40. Xing, E. P., Ng, A. Y., Jordan, M. I., & Russell, S. (2003). Distance metric learning, with application to clustering with side-information. In Advances in neural information processing systems 15.
41. Yan, R., Zhang, J., Yang, J., & Hauptmann, A. G. (2004). A discriminative learning framework with pairwise constraints for video object classification. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition (CVPR) (Vol. 2, pp. 284-291).
42. Yu, S., & Shi, J. (2003). Multiclass spectral clustering. In International conference on computer vision (pp. 313-319).
43. S. Yu J. Shi 2004 Segmentation given partial grouping constraints IEEE Transactions on Pattern Analysis and Machine Intelligence 26 2 173 183