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The improved non-negative Matrix Factorization algorithm for document clustering

Non-negative Matrix Factorization (NMF) is one latest presented approach for obtaining document clusters, which aimed to provide a minimum error non-negative representation of the term-document matrix. In this paper, we have extended the classical NMF approach by imposing sparseness constraints explicitly. The new model can learn much sparser matrix factorization. Also, an objective function is defined to impose the sparseness constraint, in addition to the non-negative constraint. Experimental results on real-world document datasets show that the proposed method can treat document clustering effectively and efficiently.