- Volume 27 Issue 2
Titles have been regarded as having effective clustering features, but they sometimes fail to represent the topic of a document and result in poorly generated document clusters. This study aims to improve the performance of document clustering with titles by suggesting titles in the citation bibliography as a clustering feature. Titles of original literature, titles in the citation bibliography, and an aggregation of both titles were adapted to measure the performance of clustering. Each feature was combined with three hierarchical clustering methods, within group average linkage, complete linkage, and Ward's method in the clustering experiment. The best practice case of this experiment was clustering document with features from both titles by within-groups average method.
Supported by : Catholic University Daegu
- Chung, Young-mee, and Jae Yun Lee. 2001. “Development of an unbiased measure for clustering performance.” Proceedings of the 7th conference of Korean Society for Information Management, 23-24 August, 2001, [KISTI, Seoul], 167-172.
- Guo, Qinglin, and Ming Zhang. 2009. “Multi-document automatic abstracting based on text clustering and semantic analysis.” Knowledgebased systems, 22(6): 482-485.
- Hudes, Mark L., Joyce C. McCann, and Bruce N. Ames. 2009. “Unusual clustering of coefficients of variation in published articles from a medical biochemistry department in India.” The FASEB Journal, 23(3): 706-708. https://doi.org/10.1096/fj.08-123117
- Kim, Jun-Ha and Jae Yun Lee. 2000. “A Comparative study on performance evaluation of document clustering results.” Proceedings of the 7th conference of Korean Society for Information Management, 24-25 August, 2000, [Ewha Womans Univ., Seoul], 45-50.
- Kostoff, Ronald N. J. Antonio del Río, Hector D. Cortes, Charles Smith, Andrew Smith, Caroline Wagner, Loet Leydesdorff, George Karypis, Guido Malpohl, and Rene Tshiteya 2007. “Clustering methodologies for identifying country core competencies.” Journal of Information Science, 33(1): 21-40. https://doi.org/10.1177/0165551506067124
- Kuo, June-Jei, and Hsin-Hsi Chen. 2007. “Cross-document event clustering using knowledge mining from co-reference chains.” Information Processing and Management, 43(2): 327-343. https://doi.org/10.1016/j.ipm.2006.07.016
- Staff, Chris. 2008. “Bookmark category web page classification using four indexing and clustering approaches.” Lecture notes in computer science, Vol.5149: 345-348. https://doi.org/10.1007/978-3-540-70987-9_50
- Tong, Tuanjie, Deendayal, Dinakarpandian, and Yugyung Lee. 2009. “Literature clustering using citation semantics.” Proceedings of the 42nd Hawaii international conference on system sciences. 5-9 January 2009, [HICS; Waikola, HI], 1-10.
- Zhang, Lin, Frizo Janssens, Liming Liang, and Wolfgang Glanzel. “Journal cross-citation analysis for validation and improvement of journalbased subject classification in bibliometric research.” Scientometrics, 82(3): 687-706. https://doi.org/10.1007/s11192-010-0180-1
- Zhao, Yueyang, Lei Cui and Hua Yang. 2009. “Evaluating reliability of co-citation clustering analysis in representing the research history of subject.” Scientometrics, 80(1): 91-102. https://doi.org/10.1007/s11192-008-2056-1
- Zhu, Shanfeng, Ichigaku Takigawa, Jia Zeng and Hiroshi Mamitsuka. 2009. “Field independent probabilistic model for clusteing multi-field documents.” Information Processing and Management, 45(5): 555-570. https://doi.org/10.1016/j.ipm.2009.03.005
- Usability Analysis of Structured Abstracts in Journal Articles for Document Clustering vol.29, pp.1, 2012, https://doi.org/10.3743/KOSIM.2012.29.1.331