G. Costa, R. Ortale
Dec 4, 2013
2013 12th International Conference on Machine Learning and Applications
Non-negative matrix factorization is intensively used in text clustering. We investigate its exploitation in the XML domain for clustering XML documents by structure and content into topically homogeneous groups. Non-negative matrix factorization is performed through an alternating least squares method, which incorporates expedients to attenuate the burden of large-scale factorizations. This is especially relevant when massive text-centric XML corpora are processed. Empirical evidence from a comparative evaluation on real-world XML corpora reveals that our approach overcomes several state-of-the-art competitors in effectiveness.