[1] DONG G, JAMES B. Contrast data mining: concepts, algorithms, and applications [M]. Boca Raton: CRC Press, 2013: 3-12. [2] DONG G, LI J. Efficient mining of emerging patterns: discovering trends and differences [C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2000: 43-52. [3] RAN X, DUAN L, LYU G. A contrast learning based approach for customer reviews crawling from dynamic Web pages [C]//Proceedings of the 29th National Database Conference of China. Beijing: Science Press, 2012: 52-57. (冉熙璐, 段磊, 吕广奕,等. 基于对比学习的动态网页商品评论获取方法[C]//第29届中国数据库学术会议论文集. 北京:科学出版社, 2012: 52-57.) [4] ZHANG Q, WU Y, LI T, et al. Mining product reviews based on shallow dependency parsing [C]//SIGIR 2009: Proceedings of the 2009 International Conference on Research on Development in Information Retrieval. New York: ACM Press, 2009:726-727. [5] HU M, LIU B. Mining and summarizing customer reviews [C]//Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2004: 168-177. [6] ARCHAK N, GHOSE A, IPEIROTIS P G. Show me the money!: deriving the pricing power of product features by mining consumer reviews [C]//KDD 2007: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2007: 56-65. [7] DUAN L, TANG C, DONG G, et al. Survey on emerging pattern based contrast mining and applications [J]. Journal of Computer Applications, 2012, 32(2): 304-308. (段磊, 唐常杰, DONG G,等. 基于显露模式的对比挖掘研究及应用进展[J]. 计算机应用, 2012, 32(2): 304-308.) [8] LI Y, FAN M. E-mail categorization and filtering technology based on essential emerging pattern [J]. Journal of Nanjing University: Natural Science, 2008, 44(5): 544-550. (李艳, 范明. 基于基本显露模式的电子邮件分类与过滤技术 [J]. 南京大学学报:自然科学版, 2008, 44(5): 544-550.) [9] LI J, LIU H, JAMES R D, et al. Simple rules underlying gene expression profiles of more than six subtypes of Acute Lymphoblastic Leukemia (ALL) patients [J]. Bioinformatics, 2003, 19(1): 71-78. [10] DONG G, FORE N. Discovering dynamic logical blog communities based on their distinct interest profiles [EB/OL]. [2014-12-10]. http://www.thinkmind.org/download.php?articleid=sotics_2011_2_10_30018. [11] DON A, ZHELEVA E, GREGORY M, et al. Discovering interesting usage patterns in text collections: integrating text mining with visualization [C]//Proceedings of the 16th ACM Conference on Information and Knowledge Management, New York: ACM Press, 2007: 213-222. [12] LEUNG C, CARMICHAEL C. Exploring social networks: a frequent pattern visualization approach [C]//Proceedings of the 2010 IEEE Second International Conference on Social Computing. Piscataway: IEEE Press,2010: 419-424. [13] LAVRAC N, JESENOVE D, TRDIN N. Mining spatio-temporal data of traffic accidents and spatial pattern visualization [J]. Metodoloski Zvezki, 2008, 5(1): 45-63. [14] KASER O, LEMIRE D. Tag-cloud drawing: algorithms for cloud visualization [EB/OL].[2015-01-20]. http://arxiv.org/abs/cs/0703109. [15] ZHOU L, ZHANG D. NLPIR: a theoretical framework for applying natural language processing to information retrieval [J]. Journal of the American Society for Information Science and Technology, 2003, 54(2): 115-123. |