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关联爆发主题模式挖掘方法研究综述 被引量:2

Review on Mining Methods of Correlated Bursty Topic Patterns
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摘要 简要介绍关联爆发主题模式的含义,从探测与发现爆发主题、定位爆发主题的爆发时间段以及分析和挖掘出关联的爆发主题三个方面出发,研究关联爆发主题模式挖掘的关键问题。最后,对基于文档集、基于同步文本流、以及基于异步文本流的关联爆发主题挖掘的相关研究进展进行分析。 This paper introduces the definition of correlated bursty topic patterns and studies the key issues of mining correlated bursty topic patterns such as detect bursty topics, locate bursty period of a bursty topic and discover correlated bursty topics. Finally, it analyzes the methods of mining correlated bursty topics from text collections, synchronous text streams and asynchronous text streams.
作者 黄永文
出处 《现代图书情报技术》 CSSCI 北大核心 2012年第10期28-34,共7页 New Technology of Library and Information Service
基金 国家社会科学基金项目"网络科技信息中爆发主题的监测与分析方法研究"(项目编号:09BTQ035)的研究成果之一
关键词 关联爆发主题 爆发探测 主题模式挖掘 Correlated bursty topic Bursty detection Mining topic patterns
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参考文献28

  • 1Klan D, Karnstedt M, Pislitz C, et al. Towards Burst Detection for Non - Stationary Stream Data [ EB/OL]. [ 2012 - 06 - 25 ]. ht- tp://citeseerx, ist. psu. edu/viewdoc/download? doi = 10. 1. 1. 150. 1719&rep = repl &type = pdf.
  • 2Kleinberg J. Bursty and Hierarchical Structure in Streams[ J]. Data Mining and Knowledge Discovery,2003,7 (4) : 373 - 397.
  • 3Wang X H , Zhai C X, Hu X, et al. Mining Correlated Bursty Top- ic Patterns from Coordinated Text Streams [ C ]. In : Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Dis- covery and Data Mining, San Jose, California, USA. New York: ACM,2007:784 - 793.
  • 4Yi J. Detecting Buzz from Time - Sequenced Document Streams [ C ]. In: Proceedings of the 2005 IEEE International Conference on e - Technology, e - Commerce and e - Service. Washington: IEEE Computer Society ,2005 : 347 - 352.
  • 5Fujiki T, Nanno T, Suzuki Y, et al. Identification of Bursts in a Document Stream [ J ]. Joho Shori Gakkai Kenkyu Hokoku, 2004 (23) :85 -92.
  • 6Sunehag P. Using Two - Stage Conditional Word Frequency Models to Model Word Burstiness and Motivating TF - IDF [ C ]. In : Pro- ceedings of the 1 lth International Conference for Artificial Intelli- gence and Statistic. New 3ersey:The Society for AI and Statistics, 2007 : 8 - 16.
  • 7Kotov A, Zhai C X, Sproat R. Mining Named Entities with Tempo- rally Correlated Bursts from Multilingual Web News Streams [ C ]. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, Hong Kong, China. New York:ACM, 2011:237 - 246.
  • 8钱哲怡,李芳.基于关键词和命名实体识别的新闻话题线索抽取[J].计算机应用与软件,2011,28(12):168-171. 被引量:4
  • 9Zhu Y Y, Shasha D. Efficient Elastic Burst Detection in Data Streams [ C ]. In : Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA. New York : ACM,2003 : 336 - 345.
  • 10Yuan Z J,Jia Y, Yang S Q. Online Burst Detection over HighSpeed Short Text Streams [ C ]. In: Proceedings of the 7th Interna- tional Conference on Computational Science, Beijing, China Berlin, Heidelberg: Springer - Verlag, 2007 : 717 - 725.

二级参考文献1

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同被引文献27

  • 1刘润奇,贺兴时,南夷非,王博.网络多媒体数据中舆情关联主题的挖掘方法[J].深圳大学学报(理工版),2020,37(1):72-78. 被引量:6
  • 2陈悦,陈超美,刘则渊,胡志刚,王贤文.CiteSpace知识图谱的方法论功能[J].科学学研究,2015,33(2):242-253. 被引量:7666
  • 3Kleinberg J. Bursty and Hierarchical Sructure in Streams [ J ]. Data Mining and Knowledge Discovery,2003 (7) :373 -397.
  • 4Chen Chaomei. CiteSpace II:Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literatur E J ]. Jour- nal of the American Society for Information Science and Tech- nology ,2006,57 ( 3 ) :359-377.
  • 5Leydesdorff L. Why Words and Co-words Cannot map the De- velopment of the Sciences [ J]. Journal of the American Society for Information Science and Technology, 1997,48 ( 5 ) : 418 - 427.
  • 6Inokuchi Akihiro. Washio Takashi. Motoda Hiroshi. An Apriori- Based Algorithm for Mining Frequent Substructures from Graph Data. Lecture Notes in Computer Sciences ,2000:13-23.
  • 7Lin Chun-Wei. Hong Tzung-Pei. Lu Wen-Hsiang. An Effective Tree Structure for Mining High Utility Itemsets. Expert Systems with Applications ,2011,38 ( 6 ) :7419-7424.
  • 8Rapp A, Agnihotri R, Baker Thomas L. Competitive Intelligence Collection and Use by Sales and Service Representatives: How Managers" Recognition and Autonomy Moderate Individual Per- formance [J].Journal of the Academy of Marketing Science, 2015,43 (3) :357-374.
  • 9Kim M C, Chen C M. A Scientometric Review of Emerging Trends and New Developments in Recommendation Systems [ J 1- Scientometrics ,2015,104 ( 1 ) :239-263.
  • 10Su Hsin-Ning, Lee Pei-Chun. Mapping Knowledge Structure by Keyword Co-occurrence:a First Look at Journal Papers in Tech- nology Foresight [ J. Scientometircs,2010,85 ( 1 ) :65-79.

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