A Dirichlet multinomial mixture model-based approach for short text clustering

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2014, Pages 233-242

  Yin, Jianhua ^a   Wang, Jianyong ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

dirichlet multinomial mixture; gibbs sampling; short text clustering

Indexed keywords

EID: 84907033074     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2623330.2623715     Document Type: Conference Paper

References (30)

1. C. C. Aggarwal and C. Zhai. A survey of text clustering algorithms. In Mining Text Data, pages 77-128. 2012.
2. S. Banerjee, K. Ramanathan, and A. Gupta. Clustering short texts using wikipedia. In SIGIR, pages 787-788, 2007.
3. H. Becker. Identification and Characterization of Events in Social Media. PhD thesis, COLUMBIA UNIVERSITY, 2011.
4. P. Berkhin. A survey of clustering data mining techniques. In Grouping Multidimensional Data. 2006.
5. C. M. Bishop and N. M. Nasrabadi. Pattern recognition and machine learning, volume 1. springer New York, 2006.
6. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 2003.
7. C. Elkan. Clustering documents with an exponential-family approximation of the dirichlet compound multinomial distribution. In ICML, 2006.
8. B. J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315(5814):972-976, 2007.
9. G. Heinrich. Parameter estimation for text analysis. Technical report version 2.9, vsonix GmbH and University of Leipzig, 2009.
10. T. Hofmann. Probabilistic latent semantic indexing. In SIGIR , pages 50-57, 1999.
11. R. Huang, G. Yu, Z. Wang, J. Zhang, and L. Shi. Dirichlet process mixture model for document clustering with feature partition. IEEE Trans. Knowl. Data Eng., 25(8):1748-1759, 2013.
12. L. Hubert and P. Arabie. Comparing partitions. Journal of classification, 2(1):193-218, 1985.
13. A. K. Jain. Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8):651-666, 2010.
14. R. E. Madsen, D. Kauchak, and C. Elkan. Modeling word burstiness using the dirichlet distribution. In ICML, pages 545-552, 2005.
15. C. D. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval, volume 1. Cambridge University Press Cambridge, 2008.
16. A. McCallum, K. Nigam, et al. A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization, volume 752, pages 41-48. Citeseer, 1998.
17. G. McLachlan and K. Basford. Mixture Models: Inference and Applications to Clustering. Marcel Dekker, New York, 1988.
18. A. Y. Ng, M. I. Jordan, Y. Weiss, et al. On spectral clustering: Analysis and an algorithm. Advances in neural information processing systems, 2, 2002.
19. X. V. Nguyen, J. Epps, and J. Bailey. Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research, 11:2837-2854, 2010.
20. K. Nigam, A. McCallum, S. Thrun, and T. M. Mitchell. Text classification from labeled and unlabeled documents using em. Machine Learning, 39(2/3):103-134, 2000.
21. A. Rangrej, S. Kulkarni, and A. V. Tendulkar. Comparative study of clustering techniques for short text documents. In WWW (Companion Volume), pages 111-112, 2011.
22. J. D. Rennie. Improving multi-class text classification with naive Bayes. PhD thesis, Massachusetts Institute of Technology, 2001.
23. J. D. Rennie, L. Shih, J. Teevan, and D. R. Karger. Tackling the poor assumptions of naive bayes text classifiers. In ICML, pages 616-623, 2003.
24. A. Rosenberg and J. Hirschberg. V-measure: A conditional entropy-based external cluster evaluation measure. In EMNLP-CoNLL, pages 410-420, 2007.
25. G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Commun. ACM, 18(11):613-620, 1975.
26. D. Sculley. Web-scale k-means clustering. In WWW, pages 1177-1178, 2010.
27. L. Shou, Z. Wang, K. Chen, and G. Chen. Sumblr: continuous summarization of evolving tweet streams. In SIGIR, pages 533-542, 2013.
28. O. Tsur, A. Littman, and A. Rappoport. Efficient clustering of short messages into general domains. In ICWSM , 2013.
29. J. Yin. Clustering microtext streams for event identification. In IJCNLP, pages 719-725, 2013.
30. G. Yu, R. Huang, and Z. Wang. Document clustering via dirichlet process mixture model with feature selection. In KDD, pages 763-772, 2010.