Abstract
In this paper, we propose an augmented NMF model to investigate the latent features of documents. The augmented NMF model incorporates the original nonnegative matrix factorization and the local invariance assumption on the document clustering. In our experiment, first we compare our model to baseline algorithms with several benchmark datasets. Then the effectiveness of the proposed model is evaluated using datasets from CiteULike. The clustering results are compared against the subject categories from Web of Science for the CiteULike dataset. Experiments of clustering on both benchmark data sets and CiteULike datasets outperforms many state of the art clustering methods.
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Xiong, Z., Zang, Y., Jiang, X., Hu, X. (2014). Document Clustering with an Augmented Nonnegative Matrix Factorization Model. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham. https://doi.org/10.1007/978-3-319-06605-9_29
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DOI: https://doi.org/10.1007/978-3-319-06605-9_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06604-2
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