- P-ISSN1013-0799
- E-ISSN2586-2073
- KCI
클러스터링 기법을 이용한 개별문서의 지식구조 자동 생성에 관한 연구
Automatic Generation of the Local Level Knowledge Structure of a Single Document Using Clustering Methods
정영미 (연세대학교)
초록
The purpose of this study is to generate the local level knowledge structure of a single document, similar to end-of-the-book indexes and table of contents of printed material, through the use of term clustering and cluster representative term selection. Furthermore, it aims to analyze the functionalities of the knowledge structure, and to confirm the applicability of these methods in user-friendly information services. The results of the term clustering experiment showed that the performance of the Ward's method was superior to that of the fuzzy K-means clustering method. In the cluster representative term selection experiment, using the highest passage frequency term as the representative yielded the best performance. Finally, the result of user task-based functionality tests illustrate that the automatically generated knowledge structure in this study functions similarly to the local level knowledge structure presented in printed material.攀＊＊＊ 본 연구는 연세대학교 대학원 박사학위논문의 일부를 요약한 것임.＊＊＊ 日本 慶應義塾大學(Keio University) 圖書館情報學科 訪問硏究員(libinfo@yonsei.ac.kr)＊＊＊＊연세대학교 문헌정보학과 교수(ymchung@yonsei.ac.kr) 논문접수일자 : 2004년 8월 17일 게재확정일자 : 2004년 9월 10일攀攀
- keywords
- 용어 클러스터링, 클러스터 대표어, 지역적 지식구조, 워드 기법, 퍼지 K-means 클러스터링 기법, term clustering, cluster representative term, local level knowledge structure, Ward's method, fuzzy K-means clustering method
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