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Document Clustering Based on Spectral Clustering and Non-negative Matrix Factorization

  • Lei Bao
  • Sheng Tang
  • Jintao Li
  • Yongdong Zhang
  • Wei-ping Ye
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)

Abstract

In this paper, we propose a novel non-negative matrix factorization (NMF) to the affinity matrix for document clustering, which enforces non-negativity and orthogonality constraints simultaneously. With the help of orthogonality constraints, this NMF provides a solution to spectral clustering, which inherits the advantages of spectral clustering and presents a much more reasonable clustering interpretation than the previous NMF-based clustering methods. Furthermore, with the help of non-negativity constraints, the proposed method is also superior to traditional eigenvector-based spectral clustering, as it can inherit the benefits of NMF-based methods that the non-negative solution is institutive, from which the final clusters could be directly derived. As a result, the proposed method combines the advantages of spectral clustering and the NMF-based methods together, and hence outperforms both of them, which is demonstrated by experimental results on TDT2 and Reuters-21578 corpus.

Keywords

Document Clustering Spectral Clustering Non-negative Matrix Factorization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Lei Bao
    • 1
    • 2
  • Sheng Tang
    • 2
  • Jintao Li
    • 2
  • Yongdong Zhang
    • 2
  • Wei-ping Ye
    • 1
  1. 1.Beijing Normal UniversityBeijingChina
  2. 2.Key Laboratory of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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