Document clustering based on non-negative matrix factorization

Authors:
Wei Xu;Xin Liu;Yihong Gong
Affiliations:
NEC Laboratories America, Inc., Cupertino, CA;NEC Laboratories America, Inc., Cupertino, CA;NEC Laboratories America, Inc., Cupertino, CA
Venue:
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Year:
2003

Citing 5
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Abstract

In this paper, we propose a novel document clustering method based on the non-negative factorization of the term-document matrix of the given document corpus. In the latent semantic space derived by the non-negative matrix factorization (NMF), each axis captures the base topic of a particular document cluster, and each document is represented as an additive combination of the base topics. The cluster membership of each document can be easily determined by finding the base topic (the axis) with which the document has the largest projection value. Our experimental evaluations show that the proposed document clustering method surpasses the latent semantic indexing and the spectral clustering methods not only in the easy and reliable derivation of document clustering results, but also in document clustering accuracies.