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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 ORD ID 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.

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