- P-ISSN1013-0799
- E-ISSN2586-2073
- KCI
텍스트 마이닝 기법을 이용한 연관용어 선정에 관한 실험적 연구
An Experimental Study on Selecting Association Terms Using Text Mining Techniques
정영미 (연세대학교)
초록
이 연구에서는 전체 문헌집단으로부터 초기 질의어에 대한 연관용어 선정 시 사용할 수 있는 최적의 기법을 찾기 위해 연관규칙 마이닝과 용어 클러스터링 기법을 이용하여 연관용어 선정 실험을 수행하였다. 연관규칙 마이닝 기법에서는 Apriori 알고리즘을 사용하였으며, 용어 클러스터링 기법에서는 연관성 척도로 GSS 계수, 자카드계수, 코사인계수, 소칼 & 스니스 5, 상호정보량을 사용하였다. 성능평가 척도로는 연관용어 정확률과 연관용어 일치율을 사용하였으며, 실험결과 Apriori 알고리즘과 GSS 계수가 가장 좋은 성능을 나타냈다.
- keywords
- text mining, association terms, similarity measures, Apriori algorithm, term clustering, 텍스트 마이닝, 연관용어, 유사계수, Apriori 알고리즘, 용어 클러스터링
Abstract
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.
- keywords
- text mining, association terms, similarity measures, Apriori algorithm, term clustering, 텍스트 마이닝, 연관용어, 유사계수, Apriori 알고리즘, 용어 클러스터링
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