• Proceedings of the Korean Information Science Society Conference
• /
• 2002.10d
• /
• pp.364-366
• /
• 2002

본 논문에서는 텍스트 형태로 구성된 특정 생물에 대한 문헌 데이터에서 해당 생물의 주요 단백질간의 이진(binary) 관계를 추출하여 이들을 특징별로 계층적으로 군집화 함으로써 특정 현상을 나타내는 단백질간의 주요 관계를 추출하는 방법을 제시한다. 텍스트 데이터에서 단백질간의 이진관계는 기본적인 데이터마이닝 기법을 사용하여 연관규칙(association rule)의 형태로 추출하게 된다. 본 논문에서는 실험을 위해 PUBMED에서 추출한 Yeast의 주요 단백질간의 관계를 포함하고 있는 논문 데이터인 MEDLINE Abstract와 몇몇 공개 데이터베이스를 사용하였다. 실험 결과 SH3와 같이 기존에 알려진 단백질간의 단일 관계를 추출하는 것 이외에 이러한 관계들을 이용하여 클러스터링을 행한 결과 공통 현상에 작용하는 주요 단백질간의 관계들이 서로 군집화 됨을 확인 할 수 있었다. 또한 단순 이진관계가 아닌 클러스터링을 이용한 보다 상위 단계에서 단순 규칙들 간의 관계를 살펴봄으로써 단백질간의 이진관계를 추출하기 위한 데이터로 사용한 문헌 데이터에 나타나 있지 않은 1차 이상의 관계를 고찰 해 볼 수 있었다. 논문에서는 규칙 추출의 전체 과정과 함께 사용된 추출 시스템의 각 부와 데이터에 대한 설명을 다룬다.

• Journal of the Korean Society for Library and Information Science
• /
• v.35 no.4
• /
• pp.141-160
• /
• 2001

This study is to develop a new system for conducting the information retrieval automatically. The system in this study is programmed by Delphi 4.0(PASCAL) and consists of automatic indexing, clustering technique, establishing and expressing term hierarchic relation, and automatic information retrieval technique. Thus this browser system can automatically control all the processes of information searching such as representation, generation and extension of queries and construction of searching strategy and feedback searching.

• Journal of the Korean Society for information Management
• /
• v.28 no.2
• /
• pp.65-78
• /
• 2011

The purpose of this study is to identify topic areas of academic library research using two informetric methods; word clustering and Pathfinder network. For the data analysis, 139 articles published in major library and information science journals from 2005 to 2009 were collected from the Korean Science Citation Index database. The keywords that represent research topics were gathered from two sections: an and titles in references. Results showed that reference titles usefully represent topics in detail, and combinings and reference titles can produce an expanded topic map.

• Journal of the Korean Society for Library and Information Science
• /
• v.40 no.3
• /
• pp.175-190
• /
• 2006

For the recent several years, there has been much interest in lexical semantic network However it seems to be very difficult to evaluate the effectiveness and correctness of it and invent the methods for applying it into various problem domains. In order to offer the fundamental ideas about how to evaluate and utilize lexical semantic networks, we developed two automatic vol·d clustering systems, which are called system A and system B respectively. 68.455.856 words were used to learn both systems. We compared the clustering results of system A to those of system B which is extended by the lexical-semantic network. The system B is extended by reconstructing the feature vectors which are used the elements of the lexical-semantic network of 3.656 '-ha' verbs. The target data is the 'multilingual Word Net-CoroNet'. When we compared the accuracy of the system A and system B, we found that system B showed the accuracy of 46.6% which is better than that of system A. 45.3%.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.14 no.1
• /
• pp.219-230
• /
• 2003

The purpose of this study is to improve of retrieval effectiveness to clustered and filtered document through query expansion. The result of this research prove that extended queries and documents, information in encyclopedia, clustering and filtering techniques are effective to promote retrieval effectiveness.

• Journal of the Korean Society for information Management
• /
• v.20 no.3
• /
• pp.277-297
• /
• 2003

This study was conducted upon the premise that index terms display the intellectual structure of a specific subject field. In this study, and attempt was made to grasp the intellectual structure of Library and Information. Science by clustering the index terms of the journals of the related academic societies at the Library of National Assembly - such as the Journal of the Korean Society for Information Management, the Journal of the Korean Library and Information Science Society, and the Journal of the Korean Society for Library and Information Science. Through the course of the study, index term clusters were generated based on the linkage of the index terms and the frequency of co-occurrence, and moreover, time periods analysis was conducted along with studies on first-appearing terms, in order to clarify the trend and development process of the Library and Information Science. This study also analysed the difference between two intellectual structure by comparing the structure generated by index term clusters with the existing structure of traditional classification systems.

• Journal of the Korean Society for Library and Information Science
• /
• v.36 no.4
• /
• pp.175-205
• /
• 2002

Information visualization in information retrieval is a creating tool that enables us to observe, manipulate, search, navigate, explore, filter, discover, understand, interact with large volumes of data for more rapidly and far more effectively to discover hidden patterns. The focus of this study is to investigate and analyze information visualization techniques in information retrieval system in the three-schematic levels. In result, it was found that first, scientific data, documents, and retrieval result information are visualized through various techniques. Second, information visualization techniques which facilitate navigation and interaction are zoom and pan, focus+context techniques, incremental exploration, and clustering. Third, the visual metaphors used by the visualization systems are presented in the linear structure, hierarchy structure, network structure, and vector scatter structure.

• Journal of the Korean Society for information Management
• /
• v.23 no.3 s.61
• /
• pp.147-165
• /
• 2006

In this study, experiments for selection of association terms were conducted in order to discover the optimum method in selecting additional terms that are related to an initial query term. Association term sets were generated by using support, confidence, and lift measures of the Apriori algorithm, and also by using the similarity measures such as GSS, Jaccard coefficient, cosine coefficient, and Sokal & Sneath 5, and mutual information. In performance evaluation of term selection methods, precision of association terms as well as the overlap ratio of association terms and relevant documents' indexing terms were used. It was found that Apriori algorithm and GSS achieved the highest level of performances.

• Proceedings of the KIEE Conference
• /
• 2007.10a
• /
• pp.182-183
• /
• 2007

본 논문에서는 전형적인 비선형 회귀문제를 다루기 위해 슈뢰딩거 방정식에 의해 표현되는 Hilbert공간에서 수행되는 Quantum 클러스터링과 Mountain 함수를 이용하여, 수치적인 입출력데이터로부터 TSK 형태의 자동적인 퍼지 if-then 규칙의 생성방법을 제안한다. 여기서 슈뢰딩거 방정식은 분석적으로 확률함수로부터 유도되어질 수 있는 포텐셜 함수를 포함한다. 이 포텐셜의 최소점들은 데이터의 특성을 포함하는 클러스터 중심들과 관련되어진다. 그러나 이들 클러스터 중심들은 데이터의 수와 같으므로 퍼지 규칙을 생성하기 어려울 뿐만 아니라 수렴속도가 느린 문제점을 가지고 있다. 이러한 문제점들을 해결하기 위해서, 본 논문에서는 밀도 척도에 기초한 클러스터 중심의 근사적인 추정에 대해 간단하면서 효과적인 Mountain 함수를 이용하여 효과적인 클러스터 중심을 얻음과 동시에 적응 뉴로-퍼지 네트워크의 자동적인 퍼지 규칙을 생성하도록 한다. 자동차 MPG 예측문제에 대한 시뮬레이션 결과는 제안된 방법이 기존 문헌에서 제시한 예측성능보다 더 좋은 특성을 보임을 알 수 있었다.

• Journal of the Korean Society for Library and Information Science
• /
• v.44 no.2
• /
• pp.285-308
• /
• 2010

This study analyzes the intellectual structure in domestic research of the Housing field, by utilizing text mining technique. Unlike the existing research that mainly uses text clustering in statistical analyses to identify subject specialties, core authors, and relationships between research areas, this study applied author profiling and factor analysis. To supplement the analysis of intellectual structure generated by text mining, and to perform evaluation on intellectual structure itself, two professionals in the housing field were interviewed. The intellectual structure, generated through text mining, was evaluated and showed its division of valid research areas that is slightly different from the traditional intellectual structure in the housing field.