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New Technology of Library and Information Service  2011, Vol. 27 Issue (1): 63-68    DOI: 10.11925/infotech.1003-3513.2011.01.10
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Hot Research Topics Detection Based on SOM
Lu Wei, Peng Yu, Chen Wu
Center for Studies of Information Resources, Wuhan University, Wuhan 430072,China
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According to detection of hot topics in a research field, the paper proposes a method combining co-word analysis and SOM together. By analysing the co-occurrence of high-frequency keywords in the literature as input data and using SOM Toolbox for SOM clustering, the collection of hot research topics is obtained.At last a case study is done by taking traditional medicine as an example, and experimental results show that this method is efficient in the process of hot research topics detection.

Key wordsSOM      Hot research topics      Co-word analysis      Traditional medicine     
Received: 22 November 2010      Published: 12 February 2011



Cite this article:

Lu Wei, Peng Yu, Chen Wu. Hot Research Topics Detection Based on SOM. New Technology of Library and Information Service, 2011, 27(1): 63-68.

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[1] Price D J. Networks of Scientific Papers
[J]. Science, 1965,149(3683):510-515.

[2] 马费成,张勤.国内外知识管理研究热点——基于词频的统计分析
[J]. 情报学报 ,2006,25(2):163-171.

[3] Dalpé R, Gauthier E, Ippersiel M P. The State of Nanotechnology Research: Report to the National Research Council of Canada. 1997.

[4] 马费成,望俊成,陈金霞,等. 我国数字信息资源研究的热点领域:共词分析透视
[J]. 情报理论与实践 , 2007,30(4):438-443.

[5] Courtial J P. A Coword Analysis of Scientometrics

[6] Kleinberg J. Bursty and Hierarchical Structure in Streams. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2002:91-101.

[7] Chen C M. CiteSpace II: Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature
[J]. Journal of the American Society for Information Science and Technology,2006,57(3):359-377.

[8] 李文兰,杨祖国. 情报学研究主题分布的文献计量学分析
[J]. 情报科学 ,2005,23(3):396-400.

[9] Shibata Naoki, Kajikawa Yuya, Takeda Yoshiyuki, et al. Comparative Study on Methods of Detecting Research Fronts Using Different Types of Citation
[J]. Journal of the American Society for Information Science and Technology,2009,60(3):571-580.

[10] 张倩,潘云涛,武夷山. 基于Web of Science数据的图书情报学研究聚类分析
[J]. 情报杂志 ,2007,26(2):82-84.

[11] Chen C M, McCain K, White H, et al. Mapping Scientometrics (1981-2001). In: Proceedings of the 65th Asist Annual Meeting. Medford: Information Today Inc. 2002:25-34.

[12] 刘则渊.科学学理论体系建构的思考——基于科学计量学的中外科学学进展研究报告
[J]. 科学学研究 ,2006,24(1):1-11.

[13] 梁永霞,刘则渊,杨中楷,等.引文分析领域前沿与演化知识图谱
[J]. 科学学研究 ,2009,27(4):516-522.

[14] 宋旭昌. 2007年iSchool科研热点分析
[J]. 情报探索 ,2008,12(9):118-121.

[15] Courtial J.P, Callon M, Sigogneau A. The Use of Patent Titles for Identifying the Topics of Invention and Forecasting Trends
[J]. Scientometrics,1993,26(2):231-242.

[16] 安璐.基于自组织映射的期刊主题研究. 武汉:武汉大学,2009.

[17] Laboratory of Computer and Information Science. SOM Toolbox.

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