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All issues Volume 12 (2017) ITM Web Conf., 12 (2017) 05008 References
Open Access
Issue
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
Article Number 05008
Number of page(s) 6
Section Session 5: Information Processing Methods and Techniques
DOI https://doi.org/10.1051/itmconf/20171205008
Published online 05 September 2017
  1. Jiawei Han, Micheline Kamber, Jian Pei. Data Mining Concepts and Techniques Third Edition. Ming Fan, Xiaofeng Meng. China Machine Press, 2015.7 [Google Scholar]
  2. Luo,- Qi. Advancing knowledge discovery and data mining. Proceedings-1st International Workshop on Knowledge Discovery and Data Mining, WKDD, pp:3–5, 2008 [Google Scholar]
  3. Mehmed Kantardzic. Data Mining: Concepts, Models, Methods, and Algorithms, Second Edition. Xiaohai Wang, Zhigang Wu. Tsinghua University Press, 2014.12 [Google Scholar]
  4. Yu Wu. Accurate Data Mining For Big Data. Chemical Industry Press. 2014.3 [Google Scholar]
  5. Mhamdi Faouzi1, Elloumi Mourad1. A new survey on knowledge discovery and data mining. Proceedings of the 2nd International Conference on Research Challenges in Information Science, RCIS 2008, pp:427–432 [Google Scholar]
  6. Tang Zhihang, Li Zhenhui, Yang Baoan. Knowledge discovery and data mining to assist natural language understanding. Journal of Computational Information Systems, v5, n1, pp: 317–322, February, 2009 [Google Scholar]
  7. Al Fawareh, Hejab Ma’azer, Jusoh, Shaidah, Osman, Wan Rozaini Sheikh. Ambiguity in text mining. Proceedings of the International Conference on Computer and Communication Engineering 2008, ICCCE08: Global Links for Human Development, pp:1172–1176 [Google Scholar]
  8. Roncero, V.G., Costa, M.C.A., Ebecken, N.F.F..Text mining on a grid environment.WIT Transactions on Information and Communication Technologies, v 42, p 13–21, 2009 [CrossRef] [EDP Sciences] [Google Scholar]
  9. Bezdek J C. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press, 1981 [CrossRef] [Google Scholar]
  10. Wang Li Juan, Guan Shou Yi, Wang XiaoLong, Wang XiZhao. Fuzzy C Mean algorithm based on feature weights Chinese Journal of Computers, 2006, 29(1D): 1797–1803(in Chinese) [Google Scholar]
  11. Li Jie, Gao XinBo, Jiao Li Cheng. A new feature weighted fuzzy clustering algorithm. Acta Electron Sinica, 2006, 34(1), pp:89–92(in Chinese) [Google Scholar]
  12. Huang J Z, Ng M K, Rong H, Li Z. Automated variable weighting in k-means type clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5), pp:1—12 [CrossRef] [Google Scholar]
  13. Wang Qiang, Ye Yunming, Huang J Z. Fuzzy k-means with variable weighting in high dimensional data analysis/Proceedings of the 9th International Conference on Web—Age Information Management. Zhangjiajie, China, 2008, pp:365—372 [Google Scholar]
  14. Wang Jun, Wang Shi—Tong, Deng Zhao—Hong. A Novel Text Clustering Algorithm Based on Feature Weighting Distance and Soft Subspace Learning. chinese journal of computers, Vol.35 No. 8, 2012, pp:1655–1665 [CrossRef] [Google Scholar]
  15. Lu Ming-Yu, Yao Xiao-Na, Wei Shan-Ling. BBS hot topic mining algorithm based on fuzzy clustering. Dalian Haishi Daxue Xuebao/Journal of Dalian Maritime University, v34, n4, pp:52–54+58, 2008 [Google Scholar]
  16. Zhang Chenyi, Sun Jianling, Ding Yiqun. Topic mining for microblog based on MB-LDA model. Jisuanji Yanjiu yu Fazhan/Computer Research and Development, v48, n10, pp:1795–1802, October, 2011 [Google Scholar]
  17. Steyvers M, Griffiths T. Probabilistic topic models. Handbook of latent semantic analysis, 2007, 427(7), pp:424–440 [Google Scholar]
  18. Sethi A, Upadrasta B, Bangalore K. Introduction to Probabilistic Topic Modeling.2012 [Google Scholar]
  19. Wainwright M J, Jordan M I. Graphical models, exponential families, andvariational inference. Foundations and Trends in Machine Learning, 2008, 1(1–2)pp:1–305 [CrossRef] [Google Scholar]
  20. Heinrich G. Parameter estimation for text analysis. Technical report, Technical report, 2005 [Google Scholar]
  21. Blei D M, Jordan M I, et al. Variational inference for Dirichlet process mixtures. Bayesian analysis, 2006, 1(1), pp:121–143 [CrossRef] [MathSciNet] [Google Scholar]
  22. Zhao W X, Jiang J, Weng J, et al. comparing twitter and traditional media using topic model[M]. Advances in information Retrieval. Springer Berlin Heidelberg, 2011, pp:338–349 [Google Scholar]
  23. Ramage D, Hall D, Nallapati R, et al. Labeled LDA:A supervised topic model for credit attribution in multi-labeled corpora[C]. proceedings of the 2009 comference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1. Association for Computational Linguistics, 2009, pp:248–256 [Google Scholar]
  24. Zhang Chenyi, Sun Jianling, Ding Yiqun. Topic Mining for Microblog Based on MB-LDA Model. Joural of Computer Research and Development, 48(10), pp:1795–1802, 2011 [Google Scholar]
  25. R https://www.r-project.org/ [Google Scholar]

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