Earticle

초록

영어

In this paper, we propose a new research trend analysis using important word clusters and its relationship. Journals published many papers every month or week and new scientific contributions were exponentially cumulated to their database. If can analysis important words and related relationships of the papers, a change of research trend in a domain is an interesting topic in text mining. We use a Term Frequency Inverse Document Frequency (TFIDF) to extract meaningful words, the similarity of words measures using WordNet information and a document comparison approach. To measure the similarity from word lists extracted by TFIDF and differences of important word clusters and weights, the approach analyzes the research trend and visualizes the differences of research interest in same research fields. To show usefulness of proposed approach, we illustrate simulations and various results.

목차

Abstract
 1. Introduction
 2. Methods
 3. Proposed Approach
 4. Simulation and Results
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• KyoJoong Oh [ Department of Computer Science, Korea Advanced Institute of Science and Technology ]
• Chae-Gyun Lim [ Department of Computer Science, Korea Advanced Institute of Science and Technology ]
• Sung Suk Kim [ Department of Computer Science, Korea Advanced Institute of Science and Technology ]
• Ho-Jin Choi [ Department of Computer Science, Korea Advanced Institute of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605