The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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Document Clustering Method using PCA and Fuzzy Association

주성분 분석과 퍼지 연관을 이용한 문서군집 방법

  • 박선 (전북대학교 전기전자정보인력양성사업단) ;
  • 안동언 (전북대학교 전기전자컴퓨터공학부)
  • Received : 2009.12.07
  • Accepted : 2010.01.07
  • Published : 2010.04.30
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Abstract

This paper proposes a new document clustering method using PCA and fuzzy association. The proposed method can represent an inherent structure of document clusters better since it select the cluster label and terms of representing cluster by semantic features based on PCA. Also it can improve the quality of document clustering because the clustered documents by using fuzzy association values distinguish well dissimilar documents in clusters. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

본 논문은 주성분 분석과 퍼지 연관을 이용한 새로운 문서군집 방법을 제안한다. 제안된 방법은 주성분 분석의 의미특징을 이용하여 군집 레이블과 군집의 대표 용어들을 선택하기 때문에 문서군집의 내부구조를 더 잘 표현할 수 있다. 또한 퍼지연관 값을 이용한 군집은 문서군집에 유사하지 않은 문서를 더 잘 구분함으로써 문서군집의 성능을 높일 수 있다. 실험결과 제안방법을 적용한 문서군집방법이 다른 문서군집 방법에 비하여 좋은 성능을 보인다.

Keywords

References

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