Data on patent and scientific paper is considered as a useful information source for analyzing technological information and has been widely utilized. Technology big data is analyzed in various ways to identify the latest technological trends and predict future promising technologies. Clustering is one of the ways to discover new features by creating groups from technology big data. Patent includes refined bibliographic information such as patent classification code whereas scientific paper does not have appropriate bibliographic information for clustering. This research proposes a new approach for clustering data of scientific paper by utilizing reference titles in each scientific paper. In this approach, the reference titles are considered as textual information because each reference consists of the title of the paper that represents the core content of the paper. We collected the scientific paper data, extracted the title of the reference, and conducted clustering by measuring the text-based similarity. The results from the proposed approach are compared with the results using existing methodologies that one is the approach utilizing textual information from titles and abstracts and the other one is a citation-based approach. The suggested approach in this paper shows statistically significant difference compared to the existing approaches and it shows better clustering performance. The proposed approach will be considered as a useful method for clustering scientific papers.

1. 서 론
2. 이론적 배경
    2.1 Girvan-Newman 클러스터링
    2.2 서지결합법
3. 연구 프레임워크
    3.1 Step 1 : 데이터 수집과 데이터 전처리
    3.2 Step 2 : 데이터의 구조화
    3.3 Step 3 : 클러스터링
    3.4 Step 4 : 군집 결과의 평가
4. 결 과
    4.1 군집화 결과
    4.2 군집화 성능의 비교
    4.3 결과의 시사점과 활용
5. 결 론
References

• Inchae Park(한성대학교 스마트경영공학부) | 박인채
• Songhee Kim(동국대학교 산업시스템공학과) | 김송희
• Byungun Yoon(동국대학교 산업시스템공학과) | 윤병운 Corresponding Author