1. 1. Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4):77 84.
   2. J. Mostafa, S. Mukhopadhyay, M. Palakal, and W. Lam, “A multilevel approach to intelligent information filtering: Model, system, and evaluation,” ACM Trans. Inform. Syst., vol. 15, no. 4, pp. 368– 399, 1997
   3. S. E. Robertson and I. Soboroff, “The TREC 2002 filtering track report,” in Proc. TREC, 2002, vol. 2002, no. 3, p. 5.
   4. Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Huang, and H.-W. Hon, “Adapting ranking svm to document retrieval,” in Proc. 29th Annu. Int. ACM SIGIR Conf. Res. Develop. Inform. Retrieval, 2006, pp. 186–193.
   5. F. Beil, M. Ester, and X. Xu, “Frequent term-based text clustering,” in Proc. 8th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2002, pp. 436–442.
   6. S.-T. Wu, Y. Li, and Y. Xu, “Deploying approaches for pattern refinement in text mining,” in Proc. 6th Int. Conf. Data Min., 2006, pp. 1157–1161
   7. N. Zhong, Y. Li, and S.-T. Wu, “Effective pattern discovery for text mining,” IEEE Trans. Knowl. Data Eng., vol. 24, no. 1, pp. 30–44, Jan. 2012.
   8. J. Lafferty and C. Zhai, “Probabilistic relevance models based on document and query generation,” in Language Modeling for Information Retrieval. New York, NY, USA: Springer, 2003, pp. 1–10.
   9. L. Azzopardi, M. Girolami, and C. Van Rijsbergen, “Topic based language models for ad hoc information retrieval,” in Proc. Neural Netw. IEEE Int. Joint Conf., 2004, vol. 4, pp. 3281–3286.
   10. S. Robertson, H. Zaragoza, and M. Taylor, “Simple BM25 extension to multiple weighted fields,” in Proc. 13th ACM Int. Conf. Inform. Knowl. Manag., 2004, pp. 42–49
   11. Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Huang, and H.-W. Hon, “Adapting ranking svm to document retrieval,” in Proc. 29th Annu. Int. ACM SIGIR Conf. Res. Develop. Inform. Retrieval, 2006, pp. 186–193.
   12. X. Li and B. Liu, “Learning to classify texts using positive and unlabeled data,” in Proc. Int. Joint Conf. Artif. Intell., 2003, vol. 3, pp. 587–592.
   13. J. F€urnkranz, “A study using n-gram features for text categorization,” Austrian Res. Inst. Artif. Intell., vol. 3, no. 1998, pp. 1–10, 1998.
   14. W. B. Cavnar and J. M. Trenkle, “N-gram-based text categorization,” Ann Arbor MI, vol. 48113, no. 2, pp. 161–175, 1994
   15. Y. Xu, Y. Li, and G. Shaw, “Reliable representations for association rules,” Data Knowl. Eng., vol. 70, no. 6, pp. 555–575, 2011
   16. J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: Current status and future directions Data Min. Knowl. Discov., vol. 15, no. 1, pp. 55–86, 2007.
   17. R. J. Bayardo Jr, “Efficiently mining long patterns from databases,” in Proc. ACM Sigmod Record, 1998, vol. 27, no. 2, pp. 85–93.
   18. J.-F. Boulicaut, A. Bykowski, and C. Rigotti, “Freesets: A condensed representation of boolean data for the approximation of frequency queries,” Data Min. Knowl. Discov., vol. 7, no. 1, pp. 5– 22, 2003.
   19. A. Bykowski and C. Rigotti, “Dbc: A condensed representation of frequent patterns for efficient mining,” Inform. Syst., vol. 28, no. 8, pp. 949–977, 2003.
   20. D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993– 1022, 2003.
   
   
2. 1. Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4):77 84.
   2. J. Mostafa, S. Mukhopadhyay, M. Palakal, and W. Lam, “A multilevel approach to intelligent information filtering: Model, system, and evaluation,” ACM Trans. Inform. Syst., vol. 15, no. 4, pp. 368– 399, 1997
   3. S. E. Robertson and I. Soboroff, “The TREC 2002 filtering track report,” in Proc. TREC, 2002, vol. 2002, no. 3, p. 5.
   4. Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Huang, and H.-W. Hon, “Adapting ranking svm to document retrieval,” in Proc. 29th Annu. Int. ACM SIGIR Conf. Res. Develop. Inform. Retrieval, 2006, pp. 186–193.
   5. F. Beil, M. Ester, and X. Xu, “Frequent term-based text clustering,” in Proc. 8th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., 2002, pp. 436–442.
   6. S.-T. Wu, Y. Li, and Y. Xu, “Deploying approaches for pattern refinement in text mining,” in Proc. 6th Int. Conf. Data Min., 2006, pp. 1157–1161
   7. N. Zhong, Y. Li, and S.-T. Wu, “Effective pattern discovery for text mining,” IEEE Trans. Knowl. Data Eng., vol. 24, no. 1, pp. 30–44, Jan. 2012.
   8. J. Lafferty and C. Zhai, “Probabilistic relevance models based on document and query generation,” in Language Modeling for Information Retrieval. New York, NY, USA: Springer, 2003, pp. 1–10.
   9. L. Azzopardi, M. Girolami, and C. Van Rijsbergen, “Topic based language models for ad hoc information retrieval,” in Proc. Neural Netw. IEEE Int. Joint Conf., 2004, vol. 4, pp. 3281–3286.
   10. S. Robertson, H. Zaragoza, and M. Taylor, “Simple BM25 extension to multiple weighted fields,” in Proc. 13th ACM Int. Conf. Inform. Knowl. Manag., 2004, pp. 42–49.
   11. Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Huang, and H.-W. Hon, “Adapting ranking svm to document retrieval,” in Proc. 29th Annu. Int. ACM SIGIR Conf. Res. Develop. Inform. Retrieval, 2006, pp. 186–193.
   12. X. Li and B. Liu, “Learning to classify texts using positive and unlabeled data,” in Proc. Int. Joint Conf. Artif. Intell., 2003, vol. 3, pp. 587–592.
   13. J. F€urnkranz, “A study using n-gram features for text categorization,” Austrian Res. Inst. Artif. Intell., vol. 3, no. 1998, pp. 1–10, 1998.
   14. W. B. Cavnar and J. M. Trenkle, “N-gram-based text categorization,” Ann Arbor MI, vol. 48113, no. 2, pp. 161–175, 1994.
   15. Y. Xu, Y. Li, and G. Shaw, “Reliable representations for association rules,” Data Knowl. Eng., vol. 70, no. 6, pp. 555–575, 2011
   16. J. Han, H. Cheng, D. Xin, and X. Yan, “Frequent pattern mining: Current status and future directions,” Data Min. Knowl. Discov., vol. 15, no. 1, pp. 55–86, 2007.
   17. R. J. Bayardo Jr, “Efficiently mining long patterns from databases,” in Proc. ACM Sigmod Record, 1998, vol. 27, no. 2, pp. 85–93.
   18. J.-F. Boulicaut, A. Bykowski, and C. Rigotti, “Freesets: A condensed representation of boolean data for the approximation of frequency queries,” Data Min. Knowl. Discov., vol. 7, no. 1, pp. 5– 22, 2003
   19. A. Bykowski and C. Rigotti, “Dbc: A condensed representation of frequent patterns for efficient mining,” Inform. Syst., vol. 28, no. 8, pp. 949–977, 2003.
   20. D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993– 1022, 2003.