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

Authors:
Lei Bao;Sheng Tang;Jintao Li;Yongdong Zhang;Wei-Ping Ye
Affiliations:
Beijing Normal University, Beijing, China 100875 and Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100080;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100080;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100080;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100080;Beijing Normal University, Beijing, China 100875
Venue:
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Year:
2008

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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.