On-line LDA: Adaptive topic models for mining text streams with applications to topic detection and tracking

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2008, Pages 3-12

  AlSumait, Loulwah ^a   Barbará, Daniel ^a   Domeniconi, Carlotta ^a  

^a George Mason University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATASETS; CURRENT MODELS; EMERGING TOPICS; EMPIRICAL BAYES METHOD; LATENT DIRICHLET ALLOCATIONS; MODELING FRAMEWORKS; ON-LINE FASHION; REAL TIME; TEXT STREAMS; TOPIC DETECTION AND TRACKING; TOPIC MODEL;

MINING;

INFORMATION MANAGEMENT;

EID: 67049100147     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2008.140     Document Type: Conference Paper

References (17)

1. C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
2. D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," The Journal of Machine Learning Research, vol.3, pp. 993-1022, 2003.
3. D. M. Blei and J. D. Lafferty, "Dynamic topic models," In International conference on Machine learning, pp. 113-120, 2006.
4. B. Cao, D. Shen, J. Sun, X. Wang, Q. Yang, and Z. Chen, "Detect and Track Latent Factors with Online Nonnegative Matrix Factorization," the 12th International Joint Conference on Artificial Inteligence, pp. 2689-2694, 2007.
5. T. Chou and M. Ch. Chen, "Using Incremental PLSI for Threshold-Resilient Online Event Analysis," the IEEE Transactions On Knowledge And Data Engineering, vol.20, no. 3, 2008.
6. T. L. Griffiths and M. Steyvers, "Finding scientific topics," Proceeding of the National Academy of Sciences, pp. 5228- 5235, 2004. (Pubitemid 38469131)
7. R. Guha, R. Kumar, D. Sivakumar, and S. Jose, "Unweaving aWeb of Documents," Proceeding of the 11th ACMSIGKDD International Conference on Knowledge Discovery in Data Mining, 2005
8. G. Heinrich, "Parameter estimation for text analysis," Arbylon publications. Retrieved from: http://www.arbylon.net/publications/, Retrieved date: 12/9/2007, Aug 2005.
9. T. Hofmann, "Probablistic Latent Semantic Indexing," Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, 1999.
10. J. Kleinberg, "Bursty and hierarchical structure in streams," Proceeding of the 8th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 2002.
11. Q. Mei and C. Zhai, "Discovering evolutionary theme patterns from text: an exploration of temporal text mining," Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005.
12. R. Nallapati, S. Ditmore, J.D. Lafferty, and K. Ung, "Multiscale topic tomography," Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery in data mining, 2007.
13. X. Song, C.-Y. Lin, B. L. Tseng, and M.-T. Sun, "Modeling and predicting personal information dissemination behavior," Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005.
14. M. Steyvers and T. L. Griffiths, "Probabilistic Topic Models," In T. Landauer, D McNamara, S. Dennis, and W. Kintsch (ed), Latent Semantic Analysis: A Road to Meaning, Laurence Erlbaum, 2005.
15. C. Wang, D. Blei, and D. Heckerman, "Continuous time dynamic topic models," The 23rd Conference on Uncertainty in Artificial Intelligence, 2008.
16. X. Wang and A. McCallum, "Topics over Time: A Non- Markov Continuous-Time Model of Topical Trends," ACM SIGKDD international conference on Knowledge discovery in data mining, 2006.
17. X. Wang, N. Mohanty, and A. McCallum, "Group and topic discovery from relations and text," The 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Workshop on Link Discovery: Issuesissues, Approaches and Applications, pp. 28-35, 2005.