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

In this paper, we propose a novel non-negative matrix factorization (NMF) to the affinity matrix for document clustering, which enforces nonnegativity 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.