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Semantic-constraint graph dual non-negative matrix factorization in text co-clustering
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Paper Abstract

Co-clustering, an extension of one-sided clustering, refers to process of clustering data points and features simultaneously. During text clustering tasks, traditional one-sided clustering algorithms have encountered difficulties dealing with sparse problem. Instead, a co-clustering procedure, where data's common organizing form is a big matrix aggregated by data points, has proved more useful when faced with sparsity. Based on the traditional co-clustering approaches, a new model named SC-DNMF, which takes into account the semantic constraints between words, is proposed in this paper. Experiments on several datasets indicate that our proposal improves the clustering accuracy over traditional co-clustering models.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211Y (27 November 2019); doi: 10.1117/12.2541938
Show Author Affiliations
Yu Liu, East China Normal Univ. (China)
Jiaxun Hua, East China Normal Univ. (China)
Youguang Chen, East China Normal Univ. (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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