On evolutionary spectral clustering

ACM Transactions on Knowledge Discovery from Data

Volumn 3, Issue 4, 2009, Pages

  Chi, Yun ^a   Song, Xiaodan ^b   Zhou, Dengyong ^c   Hino, Koji ^a   Tseng, Belle L ^d  

^a NEC LABORATORIES AMERICA   (United States)

^b GOOGLE INC   (United States)

^c MICROSOFT RESEARCH   (United States)

^d YAHOO INC   (United States)

Author keywords

Evolutionary spectral clustering; Preserving cluster membership; Preserving cluster quality; Temporal smoothness

Indexed keywords

CLUSTER MEMBERSHIPS; CLUSTERING DATA; CLUSTERING QUALITY; CLUSTERING RESULTS; CURRENT DATA; EVOLUTIONARY CLUSTERING; K-MEANS CLUSTERING; OPTIMAL SOLUTIONS; PERFORMANCE EXPERIMENT; RESEARCH AREAS; ROBUST CLUSTERING; SPECTRAL CLUSTERING; SPECTRAL CLUSTERING METHODS; SYNTHETIC DATASETS;

COST FUNCTIONS; WATER SUPPLY SYSTEMS;

CLUSTERING ALGORITHMS;

EID: 72449164947     PISSN: 15564681     EISSN: 1556472X     Source Type: Journal    
DOI: 10.1145/1631162.1631165     Document Type: Article

References (34)

1. AGGARWAL, C. C., HAN, J., WANG, J., AND YU, P. S. 2003. A framework for clustering evolving data streams. In Proceedings of the 12th VLDB Conference.
2. ASUR, S., PARTHASARATHY, S., AND UCAR, D. 2007. An event-based framework for characterizing the evolutionary behavior of interaction graphs. In Proceedings of the 13th ACMSIGKDD Conference. ACM, New York.
3. BACH, F. R. AND JORDAN, M. I. 2006. Learning spectral clustering, with application to speech separation. J. Mach. Learn. Res. 7.
4. CHAKRABARTI, D., KUMAR, R., AND TOMKINS, A. 2006. Evolutionary clustering. In Proceedings of the 12th ACM SIGKDD Conference. ACM, New York.
5. CHARIKAR, M., CHEKURI, C., FEDER, T., AND MOTWANI, R. 1997. Incremental clustering and dynamic information retrieval. In Proceedings of the 29th STOC Conference. ACM, New York.
6. CHATFIELD, C. 2003. The Analysis of Time Series: An Introduction. Chapman & Hall/CRC.
7. CHI, Y., SONG, X., ZHOU, D., HINO, K., AND TSENG, B. L. 2007. Evolutionary spectral clustering by incorporating temporal smoothness. In Proceedings of the 13th ACM SIGKDD Conference. ACM, New York.
8. CHUNG, F. R. K. 1997. Spectral Graph Theory. American Mathematical Society.
9. DE LATHAUWER, L., DE MOOR, B., AND VANDEWALLE, J. 2000. A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21, 4.
10. DHILLON, I. S., GUAN, Y., AND KULIS, B. 2004. Kernel k-means: Spectral clustering and normalized cuts. In Proceedings of the 10th ACM SIGKDD Conference.
11. DING, C. AND HE, X. 2004. K-means clustering via principal component analysis. In Proceedings of the 21st ICML Conference.
12. FAN, K. 1949. On a theorem of weyl concerning eigenvalues of linear transformations. In Proceedings of the National Academy of Science.
13. GOLUB, G. AND LOAN, C. V. 1996. Matrix Computations, third ed. Johns Hopkins University Press.
14. GUHA, S., MISHRA, N., MOTWANI, R., AND O'CALLAGHAN, L. 2000. Clustering data streams. In Proceedings of the IEEE Symposium on Foundations of Computer Science. IEEE Computer Society Press, Los Alamitos, CA.
15. GUPTA, C. ANDGROSSMAN,R. 2004. Genic:Asingle pass generalized incremental algorithm for clustering. In Proceedings of the SIAM International Conference on Data Mining. SIAM, Philadelphia, PA.
16. HUBERT, L. J. AND ARABIE, P. 1985. Comparing partitions. J. Classif. 2.
17. JI, X. AND XU, W. 2006. Document clustering with prior knowledge. In Proceedings of the SIGIR. ACM, New York.
18. LI, Y., HAN, J., AND YANG, J. 2004. Clustering moving objects. In Proceedings of the 10th ACM SIGKDD Conference. ACM, New York.
19. MEI, Q. AND ZHAI, C. 2005. Discovering evolutionary theme patterns from text: An exploration of temporal text mining. In Proceedings of the 11th ACM SIGKDD Conference. ACM, New York.
20. NEWMAN,M. E. J. AND GIRVAN,M. 2004. Finding and evaluating community structure in networks. Phys. Rev. E.
21. NG, A., JORDAN, M. AND WEISS, Y. 2001. On spectral clustering: Analysis and an algorithm. In NIPS.
22. NING, H., XU, W., CHI, Y., GONG, Y., AND HUANG, T. 2007. Incremental spectral clustering with application to monitoring of evolving blog communities. In Proceedings of the SIAM International Conference on Data Mining. SIAM, Philadelphia, PA.
23. PALLA, G., BARABASI, A.-L., AND VICSEK, T. 2007. Quantifying social group evolution. Nature 446.
24. SARKAR, P. AND MOORE, A. W. 2005. Dynamic social network analysis using latent space models. SIGKDD Explor. Newsl. 7, 2.
25. SHI, J. AND MALIK, J. 2000. Normalized cuts and image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 22, 8.
26. SPILIOPOULOU, M., NTOUTSI, I., THEODORIDIS, Y., AND SCHULT, R. 2006. Monic: Modeling and monitoring cluster transitions. In Proceedings of the 12th ACM SIGKDD Conference. ACM, New York.
27. SUN, J., FALOUTSOS, C., PAPADIMITRIOU, S., AND YU, P. S. 2007. GraphScope: Parameter-free mining of large time-evolving graphs. In Proceedings of the 13th ACM SIGKDD Conference. ACM, New York.
28. TOYODA, M. AND KITSUREGAWA, M. 2003. Extracting evolution of web communities from a series of web archives. In HYPERTEXT '03: Proceedings of the 14th ACM Conference on Hypertext and Hypermedia. ACM, New York.
29. WAGSTAFF, K., CARDIE, C., ROGERS, S., AND SCHROEDL, S. 2001. Constrained K-means clustering with background knowledge. In Proceedings of the 18th ICML Conference.
30. WEISS, Y. 1999. Segmentation using eigenvectors: A unifying view. In ICCV '99: Proceedings of the International Conference on Computer Vision Volume2.
31. XU, W., LIU, X., AND GONG, Y. 2002. Spectral text clustering. Tech. Rep. 2002-L011, NEC Laboratories America.
32. XU, W., LIU, X., AND GONG, Y. 2003. Document clustering based on non-negative matrix factorization. In Proceedings of the 26th SIGIR Conference. ACM, New York.
33. ZHA, H., HE, X., DING, C. H. Q., GU, M., AND SIMON, H. D. 2001. Spectral relaxation for k-means clustering. In NIPS.
34. ZHOU, D., HUANG, J., AND SCHÖLKOPF, B. 2005. Learning from labeled and unlabeled data on a directed graph. In Proceedings of the 22nd ICML Conference.