LPTA: A probabilistic model for latent periodic topic analysis

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2011, Pages 904-913

  Yin, Zhijun ^a   Cao, Liangliang ^b   Han, Jiawei ^a   Zhai, Chengxiang ^a   Huang, Thomas ^c  

^a University of Illinois at Urbana Champaign   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Periodic topics; Topic modeling

Indexed keywords

COHERENT SEMANTICS; DATA SETS; FACEBOOK; FINANCIAL REPORTS; NEWS ARTICLES; PERIODIC PATTERN; PERIODIC TOPICS; PROBABILISTIC MODELS; SOCIAL MEDIA; TIME SERIES DATABASE; TOPIC ANALYSIS; TV PROGRAMS;

SOCIAL NETWORKING (ONLINE);

DATA MINING;

EID: 84857169805     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2011.96     Document Type: Conference Paper

References (32)

1. J. Allan, R. Papka, and V. Lavrenko. On-line new event detection and tracking. In SIGIR, pages 37-45, 1998.
2. H. Becker, M. Naaman, and L. Gravano. Learning similarity metrics for event identification in social media. In WSDM, pages 291-300, 2010.
3. D. M. Blei and J. D. Lafferty. Dynamic topic models. In ICML, pages 113-120, 2006.
4. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
5. D. Cai, Q. Mei, J. Han, and C. Zhai. Modeling hidden topics on document manifold. In CIKM, pages 911-920, 2008.
6. L. Chen and A. Roy. Event detection from flickr data through wavelet-based spatial analysis. In CIKM, pages 523-532, 2009.
7. M. G. Elfeky, W. G. Aref, and A. K. Elmagarmid. Periodicity detection in time series databases. IEEE Trans. Knowl. Data Eng., 17(7):875-887, 2005.
8. G. P. C. Fung, J. X. Yu, H. Liu, and P. S. Yu. Time-dependent event hierarchy construction. In KDD, pages 300-309, 2007.
9. J. Han, G. Dong, and Y. Yin. Efficient mining of partial periodic patterns in time series database. In ICDE, pages 106-115, 1999.
10. D. He and D. S. Parker. Topic dynamics: an alternative model of bursts in streams of topics. In KDD, pages 443-452, 2010.
11. T. Hofmann. Probabilistic latent semantic indexing. In SIGIR, pages 50-57, 1999.
12. T. Iwata, T. Yamada, Y. Sakurai, and N. Ueda. Online multiscale dynamic topic models. In KDD, pages 663-672, 2010.
13. J. M. Kleinberg. Bursty and hierarchical structure in streams. In KDD, pages 91-101, 2002.
14. H. W. Kuhn. The Hungarian method for the assignment problem. Naval Research Logistic Quarterly, 2:83-97, 1955.
15. M. Lahiri and T. Y. Berger-Wolf. Mining periodic behavior in dynamic social networks. In ICDM, pages 373-382, 2008.
16. M. Lahiri and T. Y. Berger-Wolf. Periodic subgraph mining in dynamic networks. Knowl. Inf. Syst., 24(3):467-497, 2010.
17. J. Leskovec, L. Backstrom, and J. M. Kleinberg. Memetracking and the dynamics of the news cycle. In KDD, pages 497-506, 2009.
18. Z. Li, B. Ding, J. Han, R. Kays, and P. Nye. Mining periodic behaviors for moving objects. In KDD, pages 1099-1108, 2010.
19. Z. Li, B. Wang, M. Li, and W.-Y. Ma. A probabilistic model for retrospective news event detection. In SIGIR, pages 106-113, 2005.
20. H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering frequent episodes in sequences. In KDD, pages 210-215, 1995.
21. Q. Mei, C. Liu, H. Su, and C. Zhai. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In WWW, pages 533-542, 2006.
22. M. Murata, H. Toda, Y. Matsuura, R. Kataoka, and T. Mochizuki. Detecting periodic changes in search intentions in a search engine. In CIKM, pages 1525-1528, 2010.
23. T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In WWW, pages 851-860, 2010.
24. E. P. Vivek, E. Pogalin, and A. W. M. Smeulders. Periodic event detection and recognition in video. In ICASSP, pages 3537-3540, 2009.
25. M. Vlachos, C. Meek, Z. Vagena, and D. Gunopulos. Identifying similarities, periodicities and bursts for online search queries. In SIGMOD Conference, pages 131-142, 2004.
26. M. Vlachos, P. S. Yu, and V. Castelli. On periodicity detection and structural periodic similarity. In SDM, 2005.
27. X. Wang and A. McCallum. Topics over time: a non-markov continuous-time model of topical trends. In KDD, pages 424-433, 2006.
28. X. Wang, C. Zhai, X. Hu, and R. Sproat. Mining correlated bursty topic patterns from coordinated text streams. In KDD, pages 784-793, 2007.
29. J. Yang and J. Leskovec. Patterns of temporal variation in online media. In WSDM, pages 177-186, 2011.
30. J. Yang, W. Wang, and P. S. Yu. Mining asynchronous periodic patterns in time series data. IEEE Trans. Knowl. Data Eng., 15(3):613-628, 2003.
31. J. Yang, W. Wang, and P. S. Yu. Mining surprising periodic patterns. Data Min. Knowl. Discov., 9(2):189-216, 2004.
32. J. Zhang, Y. Song, C. Zhang, and S. Liu. Evolutionary hierarchical dirichlet processes for multiple correlated timevarying corpora. In KDD, pages 1079-1088, 2010.