Generative models for evolutionary clustering

ACM Transactions on Knowledge Discovery from Data

Volumn 6, Issue 2, 2012, Pages

  Xu, Tianbing ^a   Zhang, Zhongfei ^a   Yu, Philip S ^b   Long, Bo ^c  

^a State University of New York at Binghamton   (United States)

^b 1120 Science and Engineering Offices   (United States)

^c YAHOO INC   (United States)

Author keywords

DPChain; Evolutionary clustering; HDP HTM; Hierarchical transition matrix; IHMS

Indexed keywords

DPCHAIN; EVOLUTIONARY CLUSTERING; HDP-HTM; HIERARCHICAL TRANSITION; IHMS;

BAYESIAN NETWORKS;

SOCIAL NETWORKING (ONLINE);

EID: 84866341270     PISSN: 15564681     EISSN: 1556472X     Source Type: Journal    
DOI: 10.1145/2297456.2297459     Document Type: Article

References (49)

1. ABOU-RJEILI, A. AND KARYPIS, G. 2006. Multilevel algorithms for partitioning power-law graphs. In Parallel and Distributed Processing Symposium (IPDPS).
2. AHMED, A. AND XING, E. P. 2009. Recovering time-varying networks of dependencies in social and biological studies. In Proc. Nat. Acad. Sci. 106, 29, 11878-11883.
3. ALDOUS, D. 1983. Exchangeability and related topics. Ecole de Probabilites de Saint-Flour XIII, 1-198.
4. ANTONIAK, C. 1974. Mixtures of Dirichlet processes with applications to bayesian nonparametric problems. Ann. of Statistics 2, 6, 1152-1174.
5. BEAL, M. J., GHAHRAMANI, Z., AND RASMUSSEN, C. E. 2002. The Infinite Hidden Markov Model. In NIPS 14.
6. BISHOP, C. M. 2007. Pattern Recognition and Machine Learning. Springer, Berlin.
7. BLACKWELL, D. AND MACQUEEN, J. 1973. Ferguson distributions via plya urn schemes. Ann. Statistics 1, 2, 353-355.
8. BLEI, D. AND LAFFERTY, J. 2006. Dynamic topic models. In In Proceedings of the 23rd International Conference on Machine Learning.
9. BLEI, D., NG, A., AND JORDAN, M. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993-1022.
10. BOYD-GRABER, J. AND BLEI, D. M. 2008. Syntactic topic models. In Neural Information Processing Systems.
11. CASELLA, G. AND GEORGE, E. I. 1992. Explaining the Gibbs sampler. The Ann. Statistician 46, 3, 167-174.
12. CHAKRABARTI, D., KUMAR, R., AND TOMKINS, A. 2006. Evolutionary clustering. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 554-560. (Pubitemid 44535554)
13. 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 international conference on Knowledge Discovery and Data Mining. ACM, 153-162. (Pubitemid 350229202)
14. DEMPSTER, A., LAIRD, N., AND RUBIN, D. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. 39, 1, 1-38.
15. ESCOBAR, M. D. AND WEST, M. 1995. Bayesian density estimation and inference using mixtures. Ann. Statistics 90, 577-588.
16. FERGUSON, T. S. 1973. A bayesian analysis of some nonparametric problems. Ann. Statistics 1, 2, 209-230.
17. FLAKE, G. W., LAWRENCE, S., AND GILES, C. L. 2000. Efficient identification of web communities. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 150-160.
18. FORTUNATO, S. 2010. Community detection in graphs. http://arxiv.org/abs/ 0906.0612.
19. FOX, E. B., SUDDERTH, E. B., JORDAN, M. I., AND WILLSKY, A. S. 2007. Developing a tempered HDPHMM for systems with state persistence. Tech. rep.
20. GAEL, J. V., SAATCI, Y., TEH, Y. W., AND GHAHRAMANI, Z. 2008. Beam sampling for the Infinite Hidden Markov model. In Proceedings of the 25th International Conference on Machine Learning.
21. GRIFFITHS, T. L. AND STEYVERS, M. 2004. Finding scientific topics. In Proceedings of the National Academy of Sciences. 5228-5235. (Pubitemid 38469131)
22. GRIFFITHS, T. L., STEYVERS, M., BLEI, D. M., AND TENENBAUM, J. B. 2005. Integrating topics and syntax. In Advances in Neural Inf. Process. Syst. 17. 537-544.
23. GRUBER, A., ROSEN-ZVI, M., AND WEISS, Y. 2007. Hidden topic Markov models. In Artificial Intelligence and Statistics.
24. HEINRICH, G. 2004. Parameter estimation for text analysis. Tech. rep.
25. ISHWARAN, H. AND JAMES, L. F. 2001. Gibbs sampling methods for stick-breaking priors. J. Amer. Stat. Assoc. 96, 453.
26. MACKAY, D. J. 1997. Ensemble learning for hidden Markov models. Tech. rep.
27. MCCALLUM, A., CORRADA-EMMANUEL, A., AND WANG, X. 2005. Topic and role discovery in social networks. In Proceedings of 19th International Joint Conference on Artificial Intelligence. 786-791.
28. NEAL, R. M. 1993. Probabilistic inference using Markov Chain Monte Carlo methods. Tech. rep. CRG-TR-93-1.
29. NEAL, R. M. 2000. Markov Chain sampling methods for Dirichlet process mixture models. J. Comput. Graph. Stat. 9, 2, 249-265.
30. NG, A. Y., JORDAN, M. I., AND WEISS, Y. 2002. On spectral clustering: Analysis and an algorithm. In NIPS 14.
31. NI, K., CARIN, L., AND DUNSON, D. 2007. Multi-task learning for sequential data via iHMMs and the nested Dirichlet process. In Proceedings of ICML. 689-696.
32. PATHAK, N., DELONG, C., BANERJEE, A., AND ERICKSON, K. 2008. Social topic models for community extraction. In Proceedings of the 2nd ACM Workshop on Social Network Mining and Analysis (SNA-KDD). ACM, New York.
33. RABINER, L. 1989. A tutorial on hiddenMarkov models and selected applications inspeech recognition. Proc. IEEE. 257-286.
34. ROSEN-ZVI, M., GRIFFITHS, T., STEYVERS, M., AND SMYTH, P. 2004. The author-topic model for authors and documents. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. AUAI Press, 487-494.
35. SETHURAMAN, J. 1994. A constructive definition of Dirichlet priors. Statistica Sinica 4, 639-650.
36. SHI, J. AND MALIK, J. 2000. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 8.
37. STREHL, A. AND GHOSH, J. 2002. Cluster ensembles - A knowledge reuse framework for combining partitionings. In Proceedings of the AAAI.
38. TEH, Y., M. JORDAN, M. B., AND BLEI, D. 2007. Hierarchical Dirichlet processes. J. Amer. Stat. Assoc. 101, 476, 1566-1581.
39. TEH, Y. W. 2007. Dirichlet processes. In Encyclopedia of Machine Learning.
40. WALLACH, H. M. 2006. Topic modeling: Beyond bag-of-words. In Proceedings of the 23rd International Conference on Machine Learning. 977-984.
41. WANG, C., BLEI, D., AND HECKERMAN, D. 2008. Continuous time dynamic topic models. In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI).
42. WANG, X. AND MCCALLUM, A. 2006. Topics over time: A non-Markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, 424-433. (Pubitemid 44535539)
43. XU, T., ZHANG, Z., YU, P., AND LONG, B. 2008a. Dirichlet process-based evolutionary clustering. In Proceedings of ICDM.
44. XU, T., ZHANG, Z., YU, P., AND LONG, B. 2008b. Evolutionary clustering by hierarchical Dirichlet process with hidden Markov state. In Proceedings of ICDM.
45. XU, Z., TRESP, V., YU, S., AND YU, K. 2008c. Nonparametric relational learning for social network analysis. In Proceedings of the 2nd ACM Workshop on Social Network Mining and Analysis (SNA-KDD). ACM, New York.
46. ZHANG, H., GILES, C. L., FOLEY, H. C., AND YEN, J. 2007a. Probabilistic community discovery using hierarchical latent Gaussian mixture model. In In Proceedings of AAAI. 663-668. (Pubitemid 350149646)
47. ZHANG, H., LI, W., WANG, X., GILES, C. L., FOLEY, H. C., AND YEN, J. 2007b. HSN-PAM: Finding hierarchical probabilistic groups from large-scale networks. In Proceedings of the 7th IEEE International Conference on Data Mining (ICDM Workshops). IEEE, Los ALamitos, CA, 27-32.
48. ZHANG, H., QIU, B., GILES, C. L., FOLEY, H. C., AND YEN, J. 2007c. An lda-based community structure discovery approach for large-scale social networks. In Proceedings of the IEEE International Conference on Intelligence and Security Informatics. 200-207. (Pubitemid 47469238)
49. ZHU, X., GHAHRAMANI, Z., AND LAFFERTY, J. 2005. Time-sensitive Dirichlet process mixture models. Tech. rep. CMU-CALD-05-104.,Carnegie Mellon University.