Topic evolution in a stream of documents

Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics

Volumn 2, Issue , 2009, Pages 855-866

  Gohr, André ^a   Hinneburg, Alexander ^b   Schult, René ^c   Spiliopoulou, Myra ^c  

^a LEIBNIZ INSTITUTE OF PLANT BIOCHEMISTRY   (Germany)

^b MARTIN LUTHER UNIVERSITY HALLE WITTENBERG   (Germany)

^c OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DOCUMENT COLLECTION; PROBABILISTIC LATENT SEMANTIC ANALYSIS; TOPIC ADAPTATION;

HYDRAULICS; TERMINOLOGY;

BIOLOGY;

EID: 72749091506     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Charu C. Aggarwal and Philip S. Yu. A framework for clustering massive text and categorical data streams. In SDM, 2006.
2. L. AlSumait, D. Barbara, and C. Domeniconi. On-line LDA: adaptive topic models for mining text streams with applications to topic detection and tracking. In ICDM, 2008.
3. A. Ascuncion, P. Smyth, and M. Welling. Asynchronous distributed learning of topic models. In NIPS, 2008.
4. D. Blei and J. Lafferty. Dynamic topic models. In ICML, 2006.
5. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent Dirichlet Allocation. J. Mach. Learn. Res., 3:993-1022, 2003.
6. J.-T. Chien and M.-S. Wu. Adaptive bayesian latent semantic analysis. IEEE Transactions on Audio, Speech, and Language Processing, 16(1):198-207, Jan. 2008.
7. Tzu-Chuan Chou and Meng Chang Chen. Using incremental PLSI for threshold-resilient online event analysis. IEEE Trans. on Knowl. and Data Eng., 20(3):289-299, 2008.
8. T.L. Griffiths and M. Steyvers. Finding scientific topics. PNAS, 101(suppl-1):5228-5235, 2004.
9. T. Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1):177-196, 2001.
10. Geoffrey J. McLachlan and Thiriyambakam Krishnan. EM Algorithm and Extensions. Wiley, 1997.
11. Qiaozhu Mei and ChengXiang Zhai. Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In SIGKDD, pages 198-207, New York, NY, USA, 2005. ACM.
12. Satoshi Morinaga and Kenji Yamanishi. Tracking dynamics of topic trends using a finite mixture model. In SIGKDD, pages 811-816. ACM, 2004.
13. Rene Schult. Comparing clustering algorithms and their influence on the evolution of labeled clusters. In DEXA, pages 650-659, 2007.
14. Rene Schult and Myra Spiliopoulou. Discovering emerging topics in unlabelled text collections. In ADBIS, pages 353-366, 2006.
15. Rene Schult and Myra Spiliopoulou. Expanding the taxonomies of bibliographic archives with persistent long-term themes. In SAC, pages 627-634, 2006.
16. Xiaodan Song, Ching-Yung Lin, Belle L. Tseng, and Ming-Ting Sun. Modeling and predicting personal information dissemination behavior. In SIGKDD, pages 479-488. ACM, 2005.
17. Myra Spiliopoulou, Irene Ntoutsi, Yannis Theodoridis, and Rene Schult. Monic: modeling and monitoring cluster transitions. In SIGKDD, pages 706-711. ACM, 2006.
18. Xuerui Wang and Andrew McCallum. Topics over time: a non-markov continuous-time model of topical trends. In SIGKDD, pages 424-433. ACM, 2006.
19. Max Welling, Chaitanya Chemudugunta, and Nathan Sutter. Deterministic latent variable models and their pitfalls. In SDM, 2008.
20. Max Welling, Y.W. Teh, and B. Kappen. Hybrid variational-MCMC inference in bayesian networks. In UAI 2008, 2008.