Document Clustering using Term reweighting based on NMF (NMF 기반의 용어 가중치 재산정을 이용한 문서군집 )

Ju-Hong Lee (이주홍); 박선

Document Clustering using Term reweighting based on NMF

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2008, 13(4), pp.11-18
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

Ju-Hong Lee ¹, 박선 ²

¹인하대학교
²목포대학교

Accredited

ABSTRACT

Document clustering is an important method for document analysis and is used in many different information retrieval applications. This paper proposes a new document clustering model using the re-weighted term based NMF(non-negative matrix factorization) to cluster documents relevant to a user’s requirement. The proposed model uses the re-weighted term by using user feedback to reduce the gap between the user’s requirement for document classification and the document clusters by means of machine. The proposed method can improve the quality of document clustering because the re-weighted terms, the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set more well. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than documents clustering methods.

KEYWORDS

문서군집(Document Clustering), 사용자 피드백(User Feedback), 비음수 행렬 분해(Non-negative Matrix Factorization), 용어의 재가중치(Re-weighted Term)

Citation status

* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65