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Vol.27 No.2
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Document Clustering Using Reference Titles

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2010, v.27 no.2, pp.241-252
https://doi.org/10.3743/KOSIM.2010.27.2.241

(2010). Document Clustering Using Reference Titles. Journal of the Korean Society for Information Management, 27(2), 241-252, https://doi.org/10.3743/KOSIM.2010.27.2.241
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Abstract

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.

keywords
문헌클러스터링, 클러스터링 자질, 클러스터링 기법, 표제, 인용, document clustering, clustering feature, clustering method, title, citation, document clustering, clustering feature, clustering method, title, citation

Reference

1.

Chung, Young-mee. (2001). Development of an unbiased measure for clustering performance (167-172). Proceedings of the 7th conference of Korean Society for Information Management. KISTI.

2.

Guo, Qinglin. (2009). Multi-document automatic abstracting based on text clustering and semantic analysis. Knowledgebased systems, 22(6), 482-485.

3.

Hudes, Mark L.. (2009). Unusual clustering of coefficients of variation in published articles from a medical biochemistry department in India. The FASEB Journal, 23(3), 706-708.

4.

Kim, Jun-Ha. (2000). A Comparative study on performance evaluation of document clustering results (45-50). Proceedings of the 7th conference of Korean Society for Information Management. Ewha Womans Univ..

5.

Kostoff, Ronald N. J.. (2007). Clustering methodologies for identifying country core competencies. Journal of Information Science, 33(1), 21-40.

6.

Kuo, June-Jei. (2007). Cross-document event clustering using knowledge mining from co-reference chains. Information Processing and Management, 43(2), 327-343.

7.

Staff, Chris. (2008). Bookmark category web page classification using four indexing and clustering approaches. Lecture notes in computer science, 5149, 345-348.

8.

Tong, Tuanjie. (2009). Literature clustering using citation semantics (1-10). Proceedings of the 42nd Hawaii international conference on system sciences. HICS.

9.

Zhang, Lin. Journal cross-citation analysis for validation and improvement of journalbased subject classification in bibliometric research. Scientometrics, 82(3), 687-706.

10.

Zhao, Yueyang. (2009). Evaluating reliability of co-citation clustering analysis in representing the research history of subject. Scientometrics, 80(1), 91-102.

11.

Zhu, Shanfeng. (2009). Field independent probabilistic model for clusteing multi-field documents. Information Processing and Management, 45(5), 555-570.

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